About the Journal
The Journal of Modern Engineering and Technology (JMET) ISSN: 3134-6642 is a double-blind, peer-reviewed international journal published online three times a year, with issues released in October, February, and June. JMET is a global forum for scientists and engineers in all modern technology and engineering aspects to publish high-quality papers. Papers of original research and innovative applications from around the world are welcome. This journal aims to cover scientific research in a broader sense and not publish a niche area of study, facilitating researchers from various verticals to publish their papers. Articles published are freely available to scientific researchers in government agencies, educators, and the broad public . We are making serious efforts to promote our journal across the globe in various ways; we are sure that our journal will act as a scientific platform for all researchers to publish their works online
Features of JMET
Plagiarism Check
The renowned Turnitin plagiarism software is employed to evaluate each paper that is submitted.
Open Access
JMET is a member of the online open access community, enabling access to published articles anytime, anywhere.
Vol. 1 No. 1 (2025) : October
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About the Journal
The Journal of Modern Engineering and Technology (JMET) ISSN: 3134-6642 is a double-blind, peer-reviewed international journal published online three times a year, with issues released in October, February, and June. JMET is a global forum for scientists and engineers in all modern technology and engineering aspects to publish high-quality papers. Papers of original research and innovative applications from around the world are welcome. This journal aims to cover scientific research in a broader sense and not publish a niche area of study, facilitating researchers from various verticals to publish their papers. Articles published are freely available to scientific researchers in government agencies, educators, and the broad public .We are making serious efforts to promote our journal across the globe in various ways; we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Publishing Timeline
Acceptance rate
65%
Review time
30 days
Submission - Decision
45 days
Plagiarism
Checked by iThenticate / Plagiarism X
Open Access
Full text freely available worldwide
About the Journal
The Journal of Modern Engineering and Technology (JMET) ISSN: 3134-6642 is a double-blind, peer-reviewed international journal published online three times a year, with issues released in October, February, and June. JMET is a global forum for scientists and engineers in all modern technology and engineering aspects to publish high-quality papers. Papers of original research and innovative applications from around the world are welcome. This journal aims to cover scientific research in a broader sense and not publish a niche area of study, facilitating researchers from various verticals to publish their papers. Articles published are freely available to scientific researchers in government agencies, educators, and the broad public .We are making serious efforts to promote our journal across the globe in various ways; we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Publishing Timeline
Acceptance rate
65%
Review time
30 days
Submission - Decision
45 days
Plagiarism
Checked by iThenticate / Plagiarism X
Open Access
Full text freely available worldwide
Submissions
Submission Preparation Checklist
As part of the submission process, authors are required to check off their submission’s compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.
- The submission has not been previously published, nor has it been before another journal for consideration (or an explanation has been provided in Comments to the Editor).
- The submission file is in OpenOffice, Microsoft Word, RTF, or WordPerfect document file format.
- Where available, URLs for the references have been provided.
- The text is single-spaced, uses a 12-point font, employs italics rather than underlining (except with URL addresses), and all illustrations, figures, and tables are placed within the text at the appropriate points rather than at the end.
- If submitting to a peer-reviewed journal section, the instructions in Ensuring a Blind Review have been followed.
- Please ensure that all references indicate their DOI numbers if they are available.
Board of Editors
Editor-in-Chief
Prof. Dr. Ahmed Hashim
Managing Editors
Assist. Prof. Dr. Nadhir Ibrahim ABDULKHALEQ
Dr. Mustafa Kadhim
Dr. Ahmed S. Hussein
College of Engineering, University of Information Technology and Communications. Iraq
Editorial Board Member
Dr. Bassoma Diallo
Dr. Wondaferaw Yohannese Chubato
Dr. Ghufran Ahmad Khan
Assist. Prof. Dr. Ahmed Alsabbagh
Dr. Ali Al-Bayaty
Dr. A. Sudha
Wavoo Wajeeha Women’s College of Arts and Science, Kayalpatnam, India
Email: sudhathanalakshmi@gmail.com
ORCHID ID: 0000-0001-6927-1413
Dr. SANA ULLAH
Department of Computer Science, Iqra National University, Pakistan
Email: dr.sanaullah@inu.edu.pk
ORCHID ID: 0000-0003-1877-797X
Author Guidelines
The Journal of Modern Engineering and Technology (JMET) publishes high-quality research papers, review articles, and case studies in all areas of modern engineering, computer science, and applied technology. Authors are requested to follow the guidelines below carefully before submitting their manuscripts.
1. Manuscript Submission
• Manuscripts should be submitted electronically through the journal’s submission system or via email to info@iieti.org
• The submission must be original and not under consideration for publication elsewhere.
• Each manuscript will undergo an initial editorial screening and a double-blind peer review process.
2. Manuscript Format
• File Type: Microsoft Word (.doc or .docx) format only.
• Length: 6–15 pages, including figures and references.
• Paper Size: A4 (21 × 29.7 cm), margins: 2.5 cm on all sides.
• Font: Times New Roman, 12 pt for text, single spacing.
• Paragraphs: Justified alignment; one line spacing between paragraphs.
3. Structure of the Manuscript
A typical manuscript should contain the following sections:
1. Title Page
• Title of the paper (concise and informative)
• Author(s) full name(s), institutional affiliation(s), and email address of the corresponding author
• ORCID ID (if available)
2. Abstract
• A single paragraph of 150–250 words summarizing the purpose, methodology, results, and conclusions of the study.
• Avoid references and undefined abbreviations.
3. Keywords
4–6 relevant keywords separated by commas.
4. Introduction
• Describe the background, significance, and objectives of the study.
• Include recent and relevant references.
5. Methodology / Materials and Methods
• Clearly explain research design, tools, datasets, simulations, or experimental methods used.
• Mention software or models if applicable (e.g., MATLAB, Python, Simulink).
6. Results and Discussion
• Present findings clearly using tables, graphs, and figures where appropriate.
• Discuss results in relation to existing literature.
7. Conclusion
Summarize the key outcomes and highlight potential applications or future work.
8. Acknowledgment (optional)
Mention funding agencies, institutional support, or contributors.
9. References
• Use the IEEE citation style (numbered in square brackets).
• Ensure that all cited works are included in the reference list.
10. Figures and Tables
• Figures and tables should be numbered sequentially (e.g., Figure 1, Table 1).
• Each must include a title and caption below the figure or above the table.
• Images should be clear, high-quality (minimum 300 dpi).
• All figures and tables must be cited within the text.
11. Equations
Equations should be written using Equation Editor or MathType and numbered consecutively in parentheses aligned to the right (e.g., (1), (2)).
12 . Ethical Standards
• Authors must follow the Publication Ethics and Plagiarism Policy of JMET.
• All submissions will be screened using Turnitin, and manuscripts with similarity above 25% will not be accepted.
13. Open Access and Copyright
• JMET is an open access journal under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
• Authors retain copyright but grant permission for others to reuse and distribute their work with proper attribution.
Details: https://creativecommons.org/licenses/by/4.0/
Focus and Scope
This journal publishes high-quality research articles across a wide range of disciplines and sub-disciplines in modern engineering and technology. The scope includes, but is not limited to, the following areas:
- Computing and Computer Engineering: Computer architecture, embedded systems, intelligent computing, information systems, bioinformatics, data mining and warehousing, big data analytics, business intelligence, and modeling under uncertainty.
- Artificial Intelligence and Machine Learning: Applications in clustering, streaming data, neural information processing, image processing, pattern recognition, and speech and signal processing.
- Communications Engineering: Digital communication systems, wireless and mobile networks, optical communications, sensor networks, IoT-based communication systems, actuators for IoT, and data visualization utilizing IoT.
- Electronics and Devices: Process and device technologies, nanometer-scale integrated circuits, microprocessor-based applications, and biomedical computing.
- Power and Energy Systems: Power system protection, digital relaying, smart grids, renewable energy integration, and cloud-based energy management.
- Advanced Technologies: Nanotechnology, advanced manufacturing technologies, virtual reality, remote sensing, telemetry, and biomedical engineering applications.
The journal particularly encourages interdisciplinary research that bridges communications engineering, computer engineering, artificial intelligence, and modern computing technologies with applied engineering domains, in order to address contemporary challenges and foster innovation in science and technology.
Publication Frequency
The Journal of Modern Engineering and Technology (JMET) is published three times a year, with issues released in October, February, and June.
Peer Review Process
The peer review process is a cornerstone of the Journal of Modern Engineering and Technology (JMET), ensuring the publication of high-quality, reliable, and original research. Every manuscript submitted to JMET undergoes a rigorous multi-stage evaluation to uphold the highest academic standards.
All submissions first undergo a Desk Review and Initial Manuscript Evaluation conducted by members of the Editorial Board to verify their relevance, originality, and compliance with journal guidelines. Manuscripts that pass this stage are then assigned to at least two independent expert reviewers for assessment under a Double-Blind Peer Review Process, where both authors’ and reviewers’ identities remain confidential.
Each reviewer evaluates the manuscript’s scientific contribution, technical soundness, clarity, and originality. Based on the reviewers’ recommendations, the editorial decision may fall into one of the following categories:
• Accepted without revision
• Accepted with minor or major revisions
• Rejected
Authors are notified of the review results via email and are required to submit their revised manuscripts within eight weeks of receiving feedback. The average turnaround time from submission to publication is approximately 12 weeks.
To ensure academic integrity, all accepted papers are subjected to a plagiarism check using Turnitin, with an acceptable similarity index of 25% or less prior to publication.
Open Access Policy
The Journal of Modern Engineering and Technology (JMET) is an open access journal, ensuring that all published articles are immediately and permanently available online for everyone to read, download, and share without restriction.
All articles in JMET are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work while permitting others to copy, distribute, transmit, adapt, and build upon the material for any purpose, even commercially, provided that appropriate credit is given to the original author(s) and source.
By adopting this license, JMET fully complies with and supports open access requirements set by funding bodies, universities, and research institutions worldwide.
Authors must ensure that any third-party copyrighted material included in their manuscripts is either used with proper permission or released under a compatible open license.
For more information about the terms of the license, please visit:
https://creativecommons.org/licenses/by/4.0/
Publication Ethics
The Journal of Modern Engineering and Technology (JMET) is committed to maintaining the highest standards of ethics in academic publishing. All parties involved in the publication process authors, reviewers, editors, and the publisher are expected to adhere to the following ethical principles and best practices.
1. Duties of Authors
• Originality and Plagiarism:
Authors must ensure that their submissions are entirely original works. Any use of others’ work or ideas must be properly cited or quoted. Plagiarism in all its forms constitutes unethical publishing behavior and is unacceptable.
• Data Integrity:
Authors must present accurate data and results. Fabrication, falsification, or manipulation of data is strictly prohibited. Raw data should be retained and made available for editorial review if requested.
• Multiple or Redundant Publication:
Authors must not submit the same manuscript to more than one journal simultaneously or publish substantially similar work elsewhere.
• Authorship:
Authorship should be limited to those who have made a significant contribution to the research. All contributors must approve the final version of the manuscript and agree to its submission.
• Acknowledgment of Sources:
Proper acknowledgment must be given to all sources that have influenced the research.
• Disclosure and Conflicts of Interest:
Authors must disclose any financial or personal relationships that could influence their work.
• Corrections and Retractions:
If an author discovers a significant error or inaccuracy in their published work, they are obligated to promptly notify the editor and cooperate in retracting or correcting the paper.
2. Duties of Editors
• Publication Decisions:
Editors are responsible for deciding which articles will be published. Their decisions are based on the paper’s quality, originality, clarity, and relevance to the journal’s scope.
• Fair Play:
Manuscripts will be evaluated based on academic merit without regard to race, gender, religion, ethnic origin, or political philosophy of the authors.
• Confidentiality:
Editors and editorial staff must not disclose information about a submitted manuscript to anyone other than the corresponding author, reviewers, and the publisher.
• Conflict of Interest:
Editors must not use unpublished information from submitted manuscripts for their own research without the author’s consent.
• Peer Review Integrity:
Editors must ensure a fair, impartial, and confidential peer-review process. Reviewers are selected based on expertise and objectivity.
3. Duties of Reviewers
• Confidentiality:
All manuscripts received for review must be treated as confidential documents.
• Objectivity:
Reviews should be conducted objectively, and reviewers should express their views clearly with supporting arguments.
• Acknowledgment of Sources:
Reviewers should identify relevant published work that has not been cited by the authors.
• Conflict of Interest:
Reviewers should not evaluate manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships with the authors or institutions.
• Timeliness:
Reviewers who feel unqualified or unable to complete the review promptly should notify the editor and withdraw from the process.
4. Publisher’s Responsibilities
The publisher of JMET is committed to ensuring that editorial decisions are independent and to supporting the journal’s integrity. The publisher works closely with editors to uphold ethical standards, address allegations of misconduct, and retract or correct published works when necessary.
5. Ethical Oversight and Misconduct Handling
• JMET follows the Committee on Publication Ethics (COPE) guidelines for handling all forms of publication misconduct.
• Allegations of plagiarism, data fabrication, or authorship manipulation will be investigated thoroughly.
• Confirmed cases of misconduct may result in rejection, retraction, and notification of the author’s institution.
Copyright & License
Authors who publish in the Journal of Modern Engineering and Technology (JMET) agree to the following terms:
• Authors retain full copyright of their work and grant the journal the right of first publication. The published work is simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing others to share, adapt, and redistribute the work with proper acknowledgment of the original authors and the journal as the source of publication.
• Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of the published version of their work (for example, inclusion in a book or institutional repository), provided that the original publication in JMET is properly cited.
• Authors are encouraged to disseminate their published work online—such as in institutional repositories, academic profiles, or personal websites—to increase visibility and citation impact, provided the source is acknowledged as Journal of Modern Engineering and Technology (JMET).
All articles published in the Journal of Modern Engineering and Technology (JMET) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Indexing & Abstraction
Journal of Modern Engineering and Technology (JMET) has been indexed by:
Plagiarism Policy
The Journal of Modern Engineering and Technology (JMET) upholds a strict policy against plagiarism to ensure the integrity and originality of all published works. Authors are fully responsible for submitting manuscripts that are free from plagiarism, duplication, and fraudulent data.
Before submission, authors are strongly advised to check their manuscripts using reliable similarity detection software to verify originality. Upon submission, all manuscripts undergo an additional plagiarism screening conducted by the editorial office using iThenticate and Turnitin. The acceptable similarity index threshold is 25% or less.
Editorial Action on Plagiarism
If plagiarism or similarity beyond the acceptable limit is detected whether by editors, peer reviewers, or editorial staff at any stage of the publication process (before or after acceptance, during editing, or at proofing), the editorial board will take the following steps:
• The author(s) will be notified and asked to revise the manuscript or properly cite and quote the original sources.
• If plagiarism is found to be extensive (≥25%), the manuscript will be rejected immediately, and the author’s institution or organization may be informed.
JMET emphasizes that plagiarism in any form including self-plagiarism, data fabrication, or verbatim copying without citation is unethical and unacceptable. Authors are encouraged to thoroughly check their manuscripts before submission using plagiarism detection tools.
Plagiarism Declaration
All authors submitting to JMET are required to complete and submit a Plagiarism Declaration Form confirming that the manuscript is free from plagiarism. This form must be uploaded as a supplementary file through the journal’s online submission system or sent directly to the editorial office at info@iieti.org
Announcements
Improving the Quality of the JMET
Dear Authors,
We will publish high-quality original/research articles only, and some review papers by invitation. We intend to maintain, and improve the high standards of excellence, visibility, and further development of the journal. The goal is to have the journal indexed in Scopus, with the continuous increase of the quality of the journal articles and visibility of the journal.
We would be more than happy to receive any suggestions for improving our Journal.
Thank you
Best Regards,
Editor-in-Chief
We are open to considering any paper suggestions from the conference organizer.
Dear Sir/Madam,
In light of the editors’ prospective vision for the magazine’s development. We are open to receiving paper suggestions from other conference organizers.
Your cooperation and attention are greatly valued.
Best Regards,
JMET Editorial Office
Manuscript Preparation
1. The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
2. The submission file is in Open Office, Microsoft Word, RTF, or WordPerfect document file format.
3. Where available, URLs for the references have been provided.
4. The text is single-spaced; uses a 12-point font; employs italics, rather than underlining (except with URL addresses); and all illustrations, figures, and tables are placed within the text at the appropriate points, rather than at the end.
5. The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines, which is found in About the Journal.
Article Processing Charges (APC)
To support the journal’s open-access publishing model and maintain high-quality editorial and production services, Journal of Modern Engineering and Technology (JMET) charges an Article Processing Charge (APC) of 50 USD for each accepted paper.
This fee covers the costs of peer review management, editing, typesetting, online hosting, and long-term archiving. Authors are required to pay the APC only after their manuscript has been accepted for publication. No submission fee is required.
Detailed instructions for completing the payment will be provided upon acceptance of your article.
Privacy Statement
Submit Your Article Here
General Information
Contact
We are pleased to assist authors, reviewers, and readers.
For any inquiries related to manuscript submission, publication process, editorial issues, or collaboration, please contact us through the following channels:
General Enquires
journal@iieti.org
Chief Editor
cheifeditor@iieti.org
Get in Touch
Contact
We are pleased to assist authors, reviewers, and readers.
For any inquiries related to manuscript submission, publication process, editorial issues, or collaboration, please contact us through the following channels:
General Enquires
journal@iieti.org
Chief Editor
cheifeditor@iieti.org
Get in Touch
Low-Latency Data Fusion and Signaling Framework for Civil Drone Monitoring Over Heterogeneous Wireless Networks
Cengiz Ayten
Postgraduate Information and Computer engineering Institute, Karabük University, Turkey
Keywords: Drone monitoring; Heterogeneous wireless networks; Data fusion; Alert latency; Low-latency communication; multi-sensor system ; Civil airspace surveillance; Real-time signaling; Cooperative detection; Python simulation
ABSTRACT:
This paper presents a low-latency data fusion and signaling model for cooperative civilian drone surveillance in heterogeneous wireless networks. To achieve reaction delay vs. detection confidence tradeoff, the system hybridizes multi-node sensing, local aggregation, and adaptive alert transmission with a tunable data fusion threshold K. A Python-based simulation platform was developed to evaluate the system behaviour under different detection ranges, fusion thresholds, and network conditions. The results show that increasing the fusion threshold K slightly increases the notification delay but significantly improves the notification stability and reduces unnecessary signaling. Detection probability was analyzed as a function of range, showing stable multi-sensor coverage up to 400 m, while cumulative distribution analysis confirmed end-to-end latency below 50 ms in all test cases. The proposed fusion-aware signaling scheme thus provides a scalable and flexible approach for real-time drone awareness in civil airspace surveillance and smart city surveillance applications
REFERENCES
1. M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Unmanned aerial vehicle with underlay spectrum sharing: A case of drone surveillance,” IEEE Wireless Commun. Lett., vol. 6, no. 4, pp. 518–521, 2017.
2. S. Hayat, E. Yanmaz, and R. Muzaffar, “Survey on unmanned aerial vehicle networks for civil applications,” Ad Hoc Networks, vol. 68, pp. 1–21, 2018.
3. X. Liu and Y. Chen, “Low-latency communications in 6G UAV networks,” IEEE Internet Things J., vol. 8, no. 7, pp. 5371–5384, 2021.
4. M. Erdelj and E. Natalizio, “UAV-assisted disaster management: Applications and open issues,” Proc. IEEE, vol. 105, no. 10, pp. 1872–1897, 2017.
5. A. Fotouhi, M. Ding, and M. Hassan, “Dynamic base station repositioning to improve coverage in UAV networks,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 563–578, 2020.
6. B. Galkin, J. Kibilda, and L. A. DaSilva, “UAVs as mobile infrastructure: Addressing battery lifetime,” IEEE Commun. Mag., vol. 57, no. 6, pp. 132–137, 2019.
7. M. Z. Chowdhury, M. Shahjalal, and Y. M. Jang, “5G wireless communication for smart cities,” ICT Express, vol. 5, no. 2, pp. 77–82, 2019.
8. J. Guo, F. R. Yu, H. Zhang, X. Li, and V. C. M. Leung, “Enabling massive IoT with UAV-based relay systems: Opportunities and challenges,” IEEE Netw., vol. 32, no. 5, pp. 144–151, 2018.
9. T. H. Luan, L. Gao, Z. Li, and D. Zhao, “UAV networks for public safety: Design, challenges, and future directions,” IEEE Access, vol. 7, pp. 42742–42754, 2019.
10. M. Al-Qudaimi, S. M. A. Shah, and H. Al-Raweshidy, “Cognitive UAV communication for low-latency drone operations,” IEEE Access, vol. 9, pp. 118340–118351, 2021.
11. L. Zhang, X. Lin, and Y. Wu, “Delay optimization in multi-hop UAV networks with adaptive scheduling,” IEEE Trans. Veh. Technol., vol. 70, no. 4, pp. 3725–3737, 2021.
12. T. T. Nguyen, P. L. Nguyen, and H. H. Nguyen, “Cooperative detection and data fusion for UAV-assisted threat awareness,” IEEE Sensors J., vol. 22, no. 2, pp. 1178–1188, 2022.
13. C. Chen, Q. Wu, and Z. Zhang, “Fog-assisted UAV monitoring system for intelligent cities,” IEEE Internet Things J., vol. 8, no. 10, pp. 8374–8385, 2021.
14. S. Li and J. Zhou, “Federated fusion learning for UAV-based surveillance,” IEEE Trans. Aerosp. Electron. Syst., vol. 58, no. 2, pp. 1450–1463, 2022.
15. F. Jameel, M. A. Javed, and H. Tabassum, “Hybrid terrestrial–aerial sensing for situational awareness,” IEEE Commun. Lett., vol. 25, no. 9, pp. 2934–2938, 2021
Published
2025-10-31
How to Cite
A. Cengiz, “Low-Latency Data Fusion and Signaling Framework for Civil Drone Monitoring Over Heterogeneous Wireless Networks,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–9, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
Low-Latency Data Fusion and Signaling Framework for Civil Drone Monitoring Over Heterogeneous Wireless Networks
Cengiz Ayten
Postgraduate Information and Computer engineering Institute, Karabük University, Turkey
Keywords: Drone monitoring; Heterogeneous wireless networks; Data fusion; Alert latency; Low-latency communication; multi-sensor system ; Civil airspace surveillance; Real-time signaling; Cooperative detection; Python simulation
ABSTRACT:
This paper presents a low-latency data fusion and signaling model for cooperative civilian drone surveillance in heterogeneous wireless networks. To achieve reaction delay vs. detection confidence tradeoff, the system hybridizes multi-node sensing, local aggregation, and adaptive alert transmission with a tunable data fusion threshold K. A Python-based simulation platform was developed to evaluate the system behaviour under different detection ranges, fusion thresholds, and network conditions. The results show that increasing the fusion threshold K slightly increases the notification delay but significantly improves the notification stability and reduces unnecessary signaling. Detection probability was analyzed as a function of range, showing stable multi-sensor coverage up to 400 m, while cumulative distribution analysis confirmed end-to-end latency below 50 ms in all test cases. The proposed fusion-aware signaling scheme thus provides a scalable and flexible approach for real-time drone awareness in civil airspace surveillance and smart city surveillance applications
REFERENCES
1. M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Unmanned aerial vehicle with underlay spectrum sharing: A case of drone surveillance,” IEEE Wireless Commun. Lett., vol. 6, no. 4, pp. 518–521, 2017.
2. S. Hayat, E. Yanmaz, and R. Muzaffar, “Survey on unmanned aerial vehicle networks for civil applications,” Ad Hoc Networks, vol. 68, pp. 1–21, 2018.
3. X. Liu and Y. Chen, “Low-latency communications in 6G UAV networks,” IEEE Internet Things J., vol. 8, no. 7, pp. 5371–5384, 2021.
4. M. Erdelj and E. Natalizio, “UAV-assisted disaster management: Applications and open issues,” Proc. IEEE, vol. 105, no. 10, pp. 1872–1897, 2017.
5. A. Fotouhi, M. Ding, and M. Hassan, “Dynamic base station repositioning to improve coverage in UAV networks,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 563–578, 2020.
6. B. Galkin, J. Kibilda, and L. A. DaSilva, “UAVs as mobile infrastructure: Addressing battery lifetime,” IEEE Commun. Mag., vol. 57, no. 6, pp. 132–137, 2019.
7. M. Z. Chowdhury, M. Shahjalal, and Y. M. Jang, “5G wireless communication for smart cities,” ICT Express, vol. 5, no. 2, pp. 77–82, 2019.
8. J. Guo, F. R. Yu, H. Zhang, X. Li, and V. C. M. Leung, “Enabling massive IoT with UAV-based relay systems: Opportunities and challenges,” IEEE Netw., vol. 32, no. 5, pp. 144–151, 2018.
9. T. H. Luan, L. Gao, Z. Li, and D. Zhao, “UAV networks for public safety: Design, challenges, and future directions,” IEEE Access, vol. 7, pp. 42742–42754, 2019.
10. M. Al-Qudaimi, S. M. A. Shah, and H. Al-Raweshidy, “Cognitive UAV communication for low-latency drone operations,” IEEE Access, vol. 9, pp. 118340–118351, 2021.
11. L. Zhang, X. Lin, and Y. Wu, “Delay optimization in multi-hop UAV networks with adaptive scheduling,” IEEE Trans. Veh. Technol., vol. 70, no. 4, pp. 3725–3737, 2021.
12. T. T. Nguyen, P. L. Nguyen, and H. H. Nguyen, “Cooperative detection and data fusion for UAV-assisted threat awareness,” IEEE Sensors J., vol. 22, no. 2, pp. 1178–1188, 2022.
13. C. Chen, Q. Wu, and Z. Zhang, “Fog-assisted UAV monitoring system for intelligent cities,” IEEE Internet Things J., vol. 8, no. 10, pp. 8374–8385, 2021.
14. S. Li and J. Zhou, “Federated fusion learning for UAV-based surveillance,” IEEE Trans. Aerosp. Electron. Syst., vol. 58, no. 2, pp. 1450–1463, 2022.
15. F. Jameel, M. A. Javed, and H. Tabassum, “Hybrid terrestrial–aerial sensing for situational awareness,” IEEE Commun. Lett., vol. 25, no. 9, pp. 2934–2938, 2021
Published
2025-10-31
How to Cite
A. Cengiz, “Low-Latency Data Fusion and Signaling Framework for Civil Drone Monitoring Over Heterogeneous Wireless Networks,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–9, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Review of TCP/IP Applications: From Web Services to the Internet of Things (IoT)
Firas Basim Al Hilali
Naif Arab University for Security Sciences, Riyadh 14812, Saudi Arabia
Keywords : TCP/IP, Web Services, IoT Communication, HTTP, IPv6, Network Security,
REST API, IoT Protocols
ABSTRACT:
This review article discusses the evolution of TCP/IP applications from conventional
web services to modern IoT ecosystems. This study examines how classic protocols like HTTP,
FTP, and SMTP influenced the web for communication and how lightweight protocols like
MQTT, CoAP, and 6LoWPAN emerged to support the restrictions of IoT devices. The two
domains have been compared in terms of communication paradigms, security requirements,
scalability concerns, and service quality. Furthermore, protocol enhancements have been
highlighted in this study, such as the use of IPv6, QUIC, DTLS, and the integration with edge
computing. It points out issues that heterogeneous networks are facing nowadays, such as
latency, energy efficiency, device authentication, and congestion control. Finally, it discusses
future directions for researchers and practitioners on safe TCP/IP extensions, AI-driven
network optimization, and 5G-enabled IoT
REFERENCES
1- T. Murkomen, “Performance, privacy, and security issues of TCP/IP at the application layer: A comprehensive survey,” GSC Advanced Research and Reviews, vol. 18, pp. 234-264, 2024.
2- K. Yasukata, “IIP: an integratable TCP/IP stack,” ACM SIGCOMM Computer Communication Review, vol. 54, pp. 21-28, 2024.
3- S.-A. Drăgușin, N. Bizon, R.-M. Teodorescu, D. Toma, R.-N. Boștinaru, and G. Anghel, “Communication Protocols in Embedded Systems for Automotive Applications: Comparative Analysis and Implementation Through Virtual Instruments,” in 2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2025, pp. 1-8.
4- H. Yang, H. Liu, X. Yuan, K. Wu, W. Ni, J. A. Zhang, et al., “Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems,” Applied Sciences, vol. 15, p. 6587, 2025.
5- A. Gupta and V. K. Chaurasiya, “Adaptive Low-Latency Split Federated Learning with Dynamic Model Partitioning in Resource-Constrained Healthcare IoT,” IEEE Transactions on Green Communications and Networking, 2025.
6- M. M. Alani, “Tcp/ip model,” in Guide to OSI and TCP/IP models, ed: Springer, 2014, pp. 19-50.
7- R. A. A. P. Soepeno, “Comprehensive Network Analysis Through a Single Main Network Architecture,” 2023.
8- J. Wijenbergh, V. Moonsamy, R. van Rijsdijk-Deij, and D. Kuijsters, “Performance comparison of DNS over HTTPS to Unencrypted DNS,” PhD dissertation, 2019.
9- K. R. Fall and W. R. Stevens, Tcp/ip illustrated vol. 1: Addison-Wesley Professional, 2012.
10- G. Fisk, M. Fisk, C. Papadopoulos, and J. Neil, “Eliminating steganography in Internet traffic with active wardens,” in International workshop on information hiding, 2002, pp. 18-35.
11- L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.
12- J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
13- R. T. Fielding and R. N. Taylor, “Principled design of the modern Web architecture,” ACM Transactions on Internet Technology (TOIT), vol. 2, no. 2, pp. 115–150, 2002.
14- T. Berners-Lee, R. Fielding, and H. Frystyk, “Hypertext Transfer Protocol – HTTP/1.0,” IETF RFC 1945, May 1996.
15- T. Dierks and E. Rescorla, “The Transport Layer Security (TLS) Protocol Version 1.2,” IETF RFC 5246, Aug. 2008.
16- A. Banks and R. Gupta, “MQTT version 3.1.1,” OASIS Standard, Oct. 2014.
17- Z. Shelby, K. Hartke, and C. Bormann, “The Constrained Application Protocol (CoAP),” IETF RFC 7252, June 2014.
18- N. Kushalnagar, G. Montenegro, and C. Schumacher, “IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN): Overview, assumptions, problem statement,” IETF RFC 4919, Aug. 2007.
19- A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.
20- S. Sicari, A. Rizzardi, L. A. Grieco, and A. Coen-Porisini, “Security, privacy and trust in Internet of Things: The road ahead,” Computer Networks, vol. 76, pp. 146–164, 2015.
21- R. Rojas-Cessa, “Experiments on Computer Networks: Quickly Knowing the Protocols in the TCP/IP Suite,” arXiv preprint arXiv:2308.01713, 2023.
22- V. Korpela, “Profinetin ja TCP/IP-tekniikan vertailu,” 2022.
23- Z.-A. Chen, T.-Z. Wang, J.-K. She, K.-Y. Qian, and Z.-A. Zeng, “An Intelligent Control Simulation Platform for Nuclear Power Plants Using TCP/IP Real-Time Communication Framework,” in International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection for Nuclear Power Plant, 2024, pp. 585-599.
24- M. Ahsan, M. J. Awan, A. Yasin, S. A. Bahaj, and H. M. F. Shehzad, “Performace evaluation of TCP cubic, compound TCP and NewReno under Windows 20H1, via 802.11 n Link to LTE Core Network,” Annals of the Romanian Society for Cell Biology, vol. 25, pp. 5357-5369, 2021.
25- V. P. Singh, M. N. Kumar, M. A. K. Misra, and P. Kuncha, IoT Communication protocols vol. 1: GCS PUBLISHERS, 2023.
26- C. M. Kozierok, The TCP/IP guide: a comprehensive, illustrated Internet protocols reference: No Starch Press, 2005.
27- P. B. Nath and M. M. Uddin, “Tcp-ip model in data communication and networking,” American Journal of Engineering Research, vol. 4, pp. 102-107, 2015.
28- N. Gopalan and B. S. Selvan, TCP/IP ILLUSTRATED: PHI Learning Pvt. Ltd., 2008.
29- Y. Chen, “Performance of Message Queue Telemetry Transport Protocol and Constrained Application Protocol in Wireless Sensor Networks,” 2017.
30- R. S. Phatak, “A Survey of Communication Protocols in IoT: MQTT, COAP, and Beyond,” International Journal of Computer Technology and Electronics Communication, vol. 8, pp. 11013-11018, 2025.
31- F. Samad, A. Abbasi, Z. A. Memon, A. Aziz, and A. Rahman, “The future of internet: IPv6 fulfilling the routing needs in internet of things,” International Journal of Future Generation Communication and Networking, vol. 11, pp. 13-22, 2018.
32- W. Shang, Y. Yu, R. Droms, and L. Zhang, “Challenges in IoT networking via TCP/IP architecture,” NDN Project, vol. 2, 2016.
33- S. M. Bellovin, “Security problems in the TCP/IP protocol suite,” ACM SIGCOMM Computer Communication Review, vol. 19, pp. 32-48, 1989.
34- Z. B. Babovic, J. Protic, and V. Milutinovic, “Web performance evaluation for internet of things applications,” IEEE Access, vol. 4, pp. 6974-6992, 2016.
35- S. Abourriche, A. Zyane, and A. Ghammaz, “Adaptation of Loss Recovery Mechanisms for improving Scalability and Quality of Service in IoT Networks,” in 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS), 2023, pp. 1-6.
36- G. Tselentis, “Towards the future Internet: emerging trends from European research,” 2010.
37- M. Aledhari, R. Razzak, B. Qolomany, A. Al-Fuqaha, and F. Saeed, “Biomedical IoT: enabling technologies, architectural elements, challenges, and future directions,” IEEE Access, vol. 10, pp. 31306-31339, 2022.
38- K. A. Da Costa, J. P. Papa, C. O. Lisboa, R. Munoz, and V. H. C. de Albuquerque, “Internet of Things: A survey on machine learning-based intrusion detection approaches,” Computer Networks, vol. 151, pp. 147-157, 2019.
39- M. Țălu, “Security and privacy in the IIoT: threats, possible security countermeasures, and future challenges,” Computing&AI Connect, vol. 2, pp. 1-10, 2025.
40- M. Rawat and G. Singal, “Surveying Technology Fusion in IoT Networks for IDS: Exploring Datasets, Tools, Challenges, and Research Prospects,” ACM Transactions on Intelligent Systems and Technology, 2025.
Published
2025-10-31
How to Cite
F. B. Al Hilali, “A Review of TCP/IP Applications: From Web Services to the Internet of Things (IoT)” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–15, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Review of TCP/IP Applications: From Web Services to the Internet of Things (IoT)
Firas Basim Al Hilali
Naif Arab University for Security Sciences, Riyadh 14812, Saudi Arabia
Keywords : TCP/IP, Web Services, IoT Communication, HTTP, IPv6, Network Security,
REST API, IoT Protocols
ABSTRACT:
This review article discusses the evolution of TCP/IP applications from conventional
web services to modern IoT ecosystems. This study examines how classic protocols like HTTP,
FTP, and SMTP influenced the web for communication and how lightweight protocols like
MQTT, CoAP, and 6LoWPAN emerged to support the restrictions of IoT devices. The two
domains have been compared in terms of communication paradigms, security requirements,
scalability concerns, and service quality. Furthermore, protocol enhancements have been
highlighted in this study, such as the use of IPv6, QUIC, DTLS, and the integration with edge
computing. It points out issues that heterogeneous networks are facing nowadays, such as
latency, energy efficiency, device authentication, and congestion control. Finally, it discusses
future directions for researchers and practitioners on safe TCP/IP extensions, AI-driven
network optimization, and 5G-enabled IoT
REFERENCES
1- T. Murkomen, “Performance, privacy, and security issues of TCP/IP at the application layer: A comprehensive survey,” GSC Advanced Research and Reviews, vol. 18, pp. 234-264, 2024.
2- K. Yasukata, “IIP: an integratable TCP/IP stack,” ACM SIGCOMM Computer Communication Review, vol. 54, pp. 21-28, 2024.
3- S.-A. Drăgușin, N. Bizon, R.-M. Teodorescu, D. Toma, R.-N. Boștinaru, and G. Anghel, “Communication Protocols in Embedded Systems for Automotive Applications: Comparative Analysis and Implementation Through Virtual Instruments,” in 2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2025, pp. 1-8.
4- H. Yang, H. Liu, X. Yuan, K. Wu, W. Ni, J. A. Zhang, et al., “Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems,” Applied Sciences, vol. 15, p. 6587, 2025.
5- A. Gupta and V. K. Chaurasiya, “Adaptive Low-Latency Split Federated Learning with Dynamic Model Partitioning in Resource-Constrained Healthcare IoT,” IEEE Transactions on Green Communications and Networking, 2025.
6- M. M. Alani, “Tcp/ip model,” in Guide to OSI and TCP/IP models, ed: Springer, 2014, pp. 19-50.
7- R. A. A. P. Soepeno, “Comprehensive Network Analysis Through a Single Main Network Architecture,” 2023.
8- J. Wijenbergh, V. Moonsamy, R. van Rijsdijk-Deij, and D. Kuijsters, “Performance comparison of DNS over HTTPS to Unencrypted DNS,” PhD dissertation, 2019.
9- K. R. Fall and W. R. Stevens, Tcp/ip illustrated vol. 1: Addison-Wesley Professional, 2012.
10- G. Fisk, M. Fisk, C. Papadopoulos, and J. Neil, “Eliminating steganography in Internet traffic with active wardens,” in International workshop on information hiding, 2002, pp. 18-35.
11- L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.
12- J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
13- R. T. Fielding and R. N. Taylor, “Principled design of the modern Web architecture,” ACM Transactions on Internet Technology (TOIT), vol. 2, no. 2, pp. 115–150, 2002.
14- T. Berners-Lee, R. Fielding, and H. Frystyk, “Hypertext Transfer Protocol – HTTP/1.0,” IETF RFC 1945, May 1996.
15- T. Dierks and E. Rescorla, “The Transport Layer Security (TLS) Protocol Version 1.2,” IETF RFC 5246, Aug. 2008.
16- A. Banks and R. Gupta, “MQTT version 3.1.1,” OASIS Standard, Oct. 2014.
17- Z. Shelby, K. Hartke, and C. Bormann, “The Constrained Application Protocol (CoAP),” IETF RFC 7252, June 2014.
18- N. Kushalnagar, G. Montenegro, and C. Schumacher, “IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN): Overview, assumptions, problem statement,” IETF RFC 4919, Aug. 2007.
19- A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.
20- S. Sicari, A. Rizzardi, L. A. Grieco, and A. Coen-Porisini, “Security, privacy and trust in Internet of Things: The road ahead,” Computer Networks, vol. 76, pp. 146–164, 2015.
21- R. Rojas-Cessa, “Experiments on Computer Networks: Quickly Knowing the Protocols in the TCP/IP Suite,” arXiv preprint arXiv:2308.01713, 2023.
22- V. Korpela, “Profinetin ja TCP/IP-tekniikan vertailu,” 2022.
23- Z.-A. Chen, T.-Z. Wang, J.-K. She, K.-Y. Qian, and Z.-A. Zeng, “An Intelligent Control Simulation Platform for Nuclear Power Plants Using TCP/IP Real-Time Communication Framework,” in International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection for Nuclear Power Plant, 2024, pp. 585-599.
24- M. Ahsan, M. J. Awan, A. Yasin, S. A. Bahaj, and H. M. F. Shehzad, “Performace evaluation of TCP cubic, compound TCP and NewReno under Windows 20H1, via 802.11 n Link to LTE Core Network,” Annals of the Romanian Society for Cell Biology, vol. 25, pp. 5357-5369, 2021.
25- V. P. Singh, M. N. Kumar, M. A. K. Misra, and P. Kuncha, IoT Communication protocols vol. 1: GCS PUBLISHERS, 2023.
26- C. M. Kozierok, The TCP/IP guide: a comprehensive, illustrated Internet protocols reference: No Starch Press, 2005.
27- P. B. Nath and M. M. Uddin, “Tcp-ip model in data communication and networking,” American Journal of Engineering Research, vol. 4, pp. 102-107, 2015.
28- N. Gopalan and B. S. Selvan, TCP/IP ILLUSTRATED: PHI Learning Pvt. Ltd., 2008.
29- Y. Chen, “Performance of Message Queue Telemetry Transport Protocol and Constrained Application Protocol in Wireless Sensor Networks,” 2017.
30- R. S. Phatak, “A Survey of Communication Protocols in IoT: MQTT, COAP, and Beyond,” International Journal of Computer Technology and Electronics Communication, vol. 8, pp. 11013-11018, 2025.
31- F. Samad, A. Abbasi, Z. A. Memon, A. Aziz, and A. Rahman, “The future of internet: IPv6 fulfilling the routing needs in internet of things,” International Journal of Future Generation Communication and Networking, vol. 11, pp. 13-22, 2018.
32- W. Shang, Y. Yu, R. Droms, and L. Zhang, “Challenges in IoT networking via TCP/IP architecture,” NDN Project, vol. 2, 2016.
33- S. M. Bellovin, “Security problems in the TCP/IP protocol suite,” ACM SIGCOMM Computer Communication Review, vol. 19, pp. 32-48, 1989.
34- Z. B. Babovic, J. Protic, and V. Milutinovic, “Web performance evaluation for internet of things applications,” IEEE Access, vol. 4, pp. 6974-6992, 2016.
35- S. Abourriche, A. Zyane, and A. Ghammaz, “Adaptation of Loss Recovery Mechanisms for improving Scalability and Quality of Service in IoT Networks,” in 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS), 2023, pp. 1-6.
36- G. Tselentis, “Towards the future Internet: emerging trends from European research,” 2010.
37- M. Aledhari, R. Razzak, B. Qolomany, A. Al-Fuqaha, and F. Saeed, “Biomedical IoT: enabling technologies, architectural elements, challenges, and future directions,” IEEE Access, vol. 10, pp. 31306-31339, 2022.
38- K. A. Da Costa, J. P. Papa, C. O. Lisboa, R. Munoz, and V. H. C. de Albuquerque, “Internet of Things: A survey on machine learning-based intrusion detection approaches,” Computer Networks, vol. 151, pp. 147-157, 2019.
39- M. Țălu, “Security and privacy in the IIoT: threats, possible security countermeasures, and future challenges,” Computing&AI Connect, vol. 2, pp. 1-10, 2025.
40- M. Rawat and G. Singal, “Surveying Technology Fusion in IoT Networks for IDS: Exploring Datasets, Tools, Challenges, and Research Prospects,” ACM Transactions on Intelligent Systems and Technology, 2025.
Published
2025-10-31
How to Cite
F. B. Al Hilali, “A Review of TCP/IP Applications: From Web Services to the Internet of Things (IoT)” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–15, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
Bio-Hybrid Plant-Inspired Soft Robot with Self-Healing and Growth-Like Motion for Environmental Adaptation
Riyadh Jasim Mohammad
Department of Computer Engineering, S.T.C., Islamic Azad University, Tehran, Iran
Keywords : Biomimetics; Soft Robotics; Self-Healing Materials; Hydrogel Actuators; Plant
Tropism; Adaptive Control
ABSTRACT:
In this paper, a new plant-inspired bio-hybrid soft robot with the capability of
emulating natural growth and self-healing behaviors for sustainable and adaptive motion is
proposed. For this, inspiration is taken from plant tropisms such as phototropism and
hydrotropism, enabling the robot to grow or bend autonomously toward light and humidity
stimuli. In the proposed robot, hydrogel-based actuators are integrated with biopolymer sensors
that detect deformation and initiate localized self-healing by way of moisture-induced polymer
crosslinking. A biophysical mathematical model describing the swelling ratio, elongation, and
healing dynamics is developed with the aim of predicting motion and recovery performance.
An enhancement of the control layer is performed by a reinforcement learning approach, where
actuation sequences are optimized to obtain a desired orientation with a minimum amount of
energy. Simulation results obtained in MATLAB show that with the proposed design, a
directional bending angle of 48° toward the light source can be achieved in 20 seconds, while
92% restoration of its mechanical strength can be achieved in 10 minutes after damage. This
work shows that robotic systems may be made sustainable and self-healing using biologically
inspired soft materials along with adaptive learning, thereby finding their application in
environmental monitoring, autonomous exploration, and precision agriculture. This work lays
the foundation for eco-intelligent robotics, in which artificial systems mimic the resilience and
flexibility of natural objects
REFERENCES
1. S. Kim, C. Laschi, and B. Trimmer, “Soft robotics: A bioinspired evolution in
robotics,” Trends in Biotechnology, vol. 31, no. 5, pp. 287–294, 2013.
2. R. F. Shepherd et al., “Multigait soft robot,” Proc. Natl. Acad. Sci. USA, vol. 108,
no. 51, pp. 20400–20403, 2011.
3. D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature,
vol. 521, pp. 467–475, 2015.
4. M. Cianchetti et al., “Biohybrid soft robots,” Nature Reviews Materials, vol. 3, no.
6, pp. 143–153, 2018.
5. O. Moulia and A. Fournier, “The power and control of plant movements,” Nature,
vol. 451, pp. 80–82, 2008.
6. T. Sadeghi et al., “Bioinspired growth-driven soft robotics,” Science Robotics, vol.
5, no. 46, eaaz3910, 2020.
7. R. A. Goriely, The Mathematics of Growth and Morphogenesis. Springer, 2017.
8. Y. Zhao et al., “Light-driven hydrogel actuators with high flexibility and rapid
response,” Adv. Funct. Mater., vol. 28, no. 45, 2018.
9. L. Wu et al., “Self-healing hydrogels for soft robotics,” ACS Appl. Mater.
Interfaces, vol. 13, no. 18, pp. 21392–21406, 2021.
10. L. Hines et al., “Soft actuators for small-scale robotics,” Adv. Mater., vol. 29, no.
13, 2017.
11. F. Iida and B. Trimmer, “Emergent compliance in soft robotic systems,” Soft
Robotics, vol. 5, no. 1, pp. 1–9, 2018.
12. J. Li et al., “pH-responsive hydrogel actuators for biomimetic motion,” ACS Appl.
Mater. Interfaces, vol. 11, pp. 41542–41552, 2019.
13. D.-G. Kim et al., “Dynamic covalent networks for thermally self-healing soft
actuators,” Smart Mater. Struct., vol. 31, no. 7, 075004, 2022.
14. A. Miriyev et al., “Self-healing soft robotic actuators with sensory feedback,” Soft
Robotics Letters, vol. 2, no. 1, pp. 44–53, 2023.
15. P. J. Flory and J. Rehner, “Statistical mechanics of cross-linked polymer
networks,” J. Chem. Phys., vol. 11, no. 11, pp. 512–520, 1943.
16. H. Yuk et al., “Hydrogel robots powered by osmotic pressure,” Nature Commun.,
vol. 11, 2020.
17. X. Wang et al., “Stimuli-responsive hydrogels for soft actuation and sensing,”
Adv. Mater., vol. 34, 2022.
18. S. K. Patel et al., “Self-healing biopolymer composites for soft robotics,”
Polymers, vol. 15, no. 3, 2023.
19. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. MIT
Press, 2018.
20. C. Finn et al., “Model-agnostic meta-learning for fast adaptation of deep
networks,” in Proc. ICML, 2017.
21. M. Y. Liu et al., “Energy-efficient control in soft actuators using reinforcement
learning,” Soft Robotics, vol. 10, no. 2, 2023.
22. L. M. Zhu et al., “Dynamic response of hydrogel actuators under dual stimuli,”
Soft Robotics, vol. 9, no. 1, pp. 56–68, 2022.
23. S. H. Park et al., “Moisture-assisted self-healing of polymer hydrogels for
autonomous robots,” Biomimetics, vol. 8, no. 3, 2023.
24. A. D. Marchese et al., “Autonomous soft robotic fish capable of escape maneuvers
using fluidic elastomer actuators,” Soft Robotics, vol. 1, no. 1, pp. 75–87, 2014.
25. R. Pfeifer and J. Bongard, How the Body Shapes the Way We Think: A New View
of Intelligence. MIT Press, 2006
Published
2025-10-31
How to Cite
R. J. Mohammad, “Bio-Hybrid Plant-Inspired Soft Robot with Self-Healing and Growth-Like Motion for Environmental Adaptation,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–12, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
Bio-Hybrid Plant-Inspired Soft Robot with Self-Healing and Growth-Like Motion for Environmental Adaptation
Riyadh Jasim Mohammad
Department of Computer Engineering, S.T.C., Islamic Azad University, Tehran, Iran
Keywords : Biomimetics; Soft Robotics; Self-Healing Materials; Hydrogel Actuators; Plant
Tropism; Adaptive Control
ABSTRACT:
In this paper, a new plant-inspired bio-hybrid soft robot with the capability of
emulating natural growth and self-healing behaviors for sustainable and adaptive motion is
proposed. For this, inspiration is taken from plant tropisms such as phototropism and
hydrotropism, enabling the robot to grow or bend autonomously toward light and humidity
stimuli. In the proposed robot, hydrogel-based actuators are integrated with biopolymer sensors
that detect deformation and initiate localized self-healing by way of moisture-induced polymer
crosslinking. A biophysical mathematical model describing the swelling ratio, elongation, and
healing dynamics is developed with the aim of predicting motion and recovery performance.
An enhancement of the control layer is performed by a reinforcement learning approach, where
actuation sequences are optimized to obtain a desired orientation with a minimum amount of
energy. Simulation results obtained in MATLAB show that with the proposed design, a
directional bending angle of 48° toward the light source can be achieved in 20 seconds, while
92% restoration of its mechanical strength can be achieved in 10 minutes after damage. This
work shows that robotic systems may be made sustainable and self-healing using biologically
inspired soft materials along with adaptive learning, thereby finding their application in
environmental monitoring, autonomous exploration, and precision agriculture. This work lays
the foundation for eco-intelligent robotics, in which artificial systems mimic the resilience and
flexibility of natural objects
REFERENCES
1. S. Kim, C. Laschi, and B. Trimmer, “Soft robotics: A bioinspired evolution in
robotics,” Trends in Biotechnology, vol. 31, no. 5, pp. 287–294, 2013.
2. R. F. Shepherd et al., “Multigait soft robot,” Proc. Natl. Acad. Sci. USA, vol. 108,
no. 51, pp. 20400–20403, 2011.
3. D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature,
vol. 521, pp. 467–475, 2015.
4. M. Cianchetti et al., “Biohybrid soft robots,” Nature Reviews Materials, vol. 3, no.
6, pp. 143–153, 2018.
5. O. Moulia and A. Fournier, “The power and control of plant movements,” Nature,
vol. 451, pp. 80–82, 2008.
6. T. Sadeghi et al., “Bioinspired growth-driven soft robotics,” Science Robotics, vol.
5, no. 46, eaaz3910, 2020.
7. R. A. Goriely, The Mathematics of Growth and Morphogenesis. Springer, 2017.
8. Y. Zhao et al., “Light-driven hydrogel actuators with high flexibility and rapid
response,” Adv. Funct. Mater., vol. 28, no. 45, 2018.
9. L. Wu et al., “Self-healing hydrogels for soft robotics,” ACS Appl. Mater.
Interfaces, vol. 13, no. 18, pp. 21392–21406, 2021.
10. L. Hines et al., “Soft actuators for small-scale robotics,” Adv. Mater., vol. 29, no.
13, 2017.
11. F. Iida and B. Trimmer, “Emergent compliance in soft robotic systems,” Soft
Robotics, vol. 5, no. 1, pp. 1–9, 2018.
12. J. Li et al., “pH-responsive hydrogel actuators for biomimetic motion,” ACS Appl.
Mater. Interfaces, vol. 11, pp. 41542–41552, 2019.
13. D.-G. Kim et al., “Dynamic covalent networks for thermally self-healing soft
actuators,” Smart Mater. Struct., vol. 31, no. 7, 075004, 2022.
14. A. Miriyev et al., “Self-healing soft robotic actuators with sensory feedback,” Soft
Robotics Letters, vol. 2, no. 1, pp. 44–53, 2023.
15. P. J. Flory and J. Rehner, “Statistical mechanics of cross-linked polymer
networks,” J. Chem. Phys., vol. 11, no. 11, pp. 512–520, 1943.
16. H. Yuk et al., “Hydrogel robots powered by osmotic pressure,” Nature Commun.,
vol. 11, 2020.
17. X. Wang et al., “Stimuli-responsive hydrogels for soft actuation and sensing,”
Adv. Mater., vol. 34, 2022.
18. S. K. Patel et al., “Self-healing biopolymer composites for soft robotics,”
Polymers, vol. 15, no. 3, 2023.
19. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. MIT
Press, 2018.
20. C. Finn et al., “Model-agnostic meta-learning for fast adaptation of deep
networks,” in Proc. ICML, 2017.
21. M. Y. Liu et al., “Energy-efficient control in soft actuators using reinforcement
learning,” Soft Robotics, vol. 10, no. 2, 2023.
22. L. M. Zhu et al., “Dynamic response of hydrogel actuators under dual stimuli,”
Soft Robotics, vol. 9, no. 1, pp. 56–68, 2022.
23. S. H. Park et al., “Moisture-assisted self-healing of polymer hydrogels for
autonomous robots,” Biomimetics, vol. 8, no. 3, 2023.
24. A. D. Marchese et al., “Autonomous soft robotic fish capable of escape maneuvers
using fluidic elastomer actuators,” Soft Robotics, vol. 1, no. 1, pp. 75–87, 2014.
25. R. Pfeifer and J. Bongard, How the Body Shapes the Way We Think: A New View
of Intelligence. MIT Press, 2006
Published
2025-10-31
How to Cite
R. J. Mohammad, “Bio-Hybrid Plant-Inspired Soft Robot with Self-Healing and Growth-Like Motion for Environmental Adaptation,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–12, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Neural Network–Based Framework for Automatic Colorization of Grayscale Historical Photographs
Omer Saad Abdulqader Abdulwahab
College of Engineering, University of Information Technology and Communication, Baghdad, Iraq
Muneer Sameer Gheni Mansoor
College of Engineering, University of Information Technology and Communication, Baghdad, Iraq
Hasanien K. Kuba
Biomedical Informatics College, University of Information Technology and Communication, Baghdad, Iraq
Keywords: Image colorization; Neural networks; MATLAB; Grayscale restoration; Deep learning; Computer vision; Historical images.
ABSTRACT:
Colorization of black-and-white images is a difficult and important problem in computer vision, with considerable applications in the restoration and preservation of historical images. This paper describes a neural network–based approach for the automatic colorization of grayscale historical images carried out purely in MATLAB. A feedforward neural network was trained with paired grayscale and colour image datasets in order to learn the mapping from luminance to chrominance components. To save computational cost, the network was trained and tested on low-resolution, downscaled images. As seen from experimental results, the proposed model can generate approximate yet visually acceptable colour reconstructions, clearly identifying key areas such as the sky, vegetation, and human skin. Although the colorized output is not fully photorealistic, the approach validates MATLAB as a powerful and accessible platform for computer vision research and prototyping, particularly in environments where Python toolsets or GPU acceleration are not feasible. This study provides an experimental and educational basis for follow-on research with potential extensions via the addition of convolutional neural networks (CNNs) and larger, more diverse datasets
REFERENCES
1. J. Wang, M. Wan, Y. Xu, X. Kong, G. Gu, and Q. Chen, “Underwater image restoration via constrained color compensation and background light color space-based haze-line model,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
2. J. Wang, M. Wan, Y. Xu, X. Kong, G. Gu, and Q. Chen, “Underwater image restoration via constrained color compensation and background light color space-based haze-line model,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
3. H. S. A. Awan and M. T. Mahmood, “Underwater image restoration through color correction and UW-Net,” Electronics, vol. 13, p. 199, 2024.
4. Q.-K. Ding and H.-E. Liang, “Digital restoration and reconstruction of heritage clothing: a review,” Heritage Science, vol. 12, p. 225, 2024.
5. A. Salmona, L. Bouza, and J. Delon, “Deoldify: A review and implementation of an automatic colorization method,” Image Processing On Line, vol. 12, pp. 347-368, 2022.
6. A. Levin, D. Lischinski, and Y. Weiss, “Colorization using optimization,” in ACM SIGGRAPH 2004 Papers, ed, 2004, pp. 689-694.
7. R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision, 2016, pp. 649-666.
8. S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification,” ACM Transactions on Graphics (ToG), vol. 35, pp. 1-11, 2016.
9. R. Zhang, J.-Y. Zhu, P. Isola, X. Geng, A. S. Lin, T. Yu, et al., “Real-time user-guided image colorization with learned deep priors,” arXiv preprint arXiv:1705.02999, 2017.
10. Y. Cao, Z. Zhou, W. Zhang, and Y. Yu, “Unsupervised diverse colorization via generative adversarial networks,” in Joint European conference on machine learning and knowledge discovery in databases, 2017, pp. 151-166.
11. V. Konovalov and V. Myasnikov, “Study of Colorization and Super-Resolution Efficiency in Image Restoration,” in 2024 X International Conference on Information Technology and Nanotechnology (ITNT), 2024, pp. 1-12>
Published
2025-10-31
How to Cite
O. S. A. Abdulwahab, M. S. G. Mansoor, and H. K. Kuba, “A neural network–based framework for automatic colorization of grayscale historical photographs,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–9, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Neural Network–Based Framework for Automatic Colorization of Grayscale Historical Photographs
Omer Saad Abdulqader Abdulwahab
College of Engineering, University of Information Technology and Communication, Baghdad, Iraq
Muneer Sameer Gheni Mansoor
College of Engineering, University of Information Technology and Communication, Baghdad, Iraq
Hasanien K. Kuba
Biomedical Informatics College, University of Information Technology and Communication, Baghdad, Iraq
Keywords: Image colorization; Neural networks; MATLAB; Grayscale restoration; Deep learning; Computer vision; Historical images.
ABSTRACT:
Colorization of black-and-white images is a difficult and important problem in computer vision, with considerable applications in the restoration and preservation of historical images. This paper describes a neural network–based approach for the automatic colorization of grayscale historical images carried out purely in MATLAB. A feedforward neural network was trained with paired grayscale and colour image datasets in order to learn the mapping from luminance to chrominance components. To save computational cost, the network was trained and tested on low-resolution, downscaled images. As seen from experimental results, the proposed model can generate approximate yet visually acceptable colour reconstructions, clearly identifying key areas such as the sky, vegetation, and human skin. Although the colorized output is not fully photorealistic, the approach validates MATLAB as a powerful and accessible platform for computer vision research and prototyping, particularly in environments where Python toolsets or GPU acceleration are not feasible. This study provides an experimental and educational basis for follow-on research with potential extensions via the addition of convolutional neural networks (CNNs) and larger, more diverse datasets
REFERENCES
1. J. Wang, M. Wan, Y. Xu, X. Kong, G. Gu, and Q. Chen, “Underwater image restoration via constrained color compensation and background light color space-based haze-line model,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
2. J. Wang, M. Wan, Y. Xu, X. Kong, G. Gu, and Q. Chen, “Underwater image restoration via constrained color compensation and background light color space-based haze-line model,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
3. H. S. A. Awan and M. T. Mahmood, “Underwater image restoration through color correction and UW-Net,” Electronics, vol. 13, p. 199, 2024.
4. Q.-K. Ding and H.-E. Liang, “Digital restoration and reconstruction of heritage clothing: a review,” Heritage Science, vol. 12, p. 225, 2024.
5. A. Salmona, L. Bouza, and J. Delon, “Deoldify: A review and implementation of an automatic colorization method,” Image Processing On Line, vol. 12, pp. 347-368, 2022.
6. A. Levin, D. Lischinski, and Y. Weiss, “Colorization using optimization,” in ACM SIGGRAPH 2004 Papers, ed, 2004, pp. 689-694.
7. R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision, 2016, pp. 649-666.
8. S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification,” ACM Transactions on Graphics (ToG), vol. 35, pp. 1-11, 2016.
9. R. Zhang, J.-Y. Zhu, P. Isola, X. Geng, A. S. Lin, T. Yu, et al., “Real-time user-guided image colorization with learned deep priors,” arXiv preprint arXiv:1705.02999, 2017.
10. Y. Cao, Z. Zhou, W. Zhang, and Y. Yu, “Unsupervised diverse colorization via generative adversarial networks,” in Joint European conference on machine learning and knowledge discovery in databases, 2017, pp. 151-166.
11. V. Konovalov and V. Myasnikov, “Study of Colorization and Super-Resolution Efficiency in Image Restoration,” in 2024 X International Conference on Information Technology and Nanotechnology (ITNT), 2024, pp. 1-12>
Published
2025-10-31
How to Cite
O. S. A. Abdulwahab, M. S. G. Mansoor, and H. K. Kuba, “A neural network–based framework for automatic colorization of grayscale historical photographs,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–9, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
DOI:
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Low-Latency Data Fusion and Signaling Framework for Civil Drone Monitoring Over Heterogeneous Wireless Networks
Cengiz Ayten
Postgraduate Information and Computer engineering Institute, Karabük University, Turkey
Keywords: Drone monitoring; Heterogeneous wireless networks; Data fusion; Alert latency; Low-latency communication; multi-sensor system ; Civil airspace surveillance; Real-time signaling; Cooperative detection; Python simulation
ABSTRACT:
This paper presents a low-latency data fusion and signaling model for cooperative civilian drone surveillance in heterogeneous wireless networks. To achieve reaction delay vs. detection confidence tradeoff, the system hybridizes multi-node sensing, local aggregation, and adaptive alert transmission with a tunable data fusion threshold K. A Python-based simulation platform was developed to evaluate the system behaviour under different detection ranges, fusion thresholds, and network conditions. The results show that increasing the fusion threshold K slightly increases the notification delay but significantly improves the notification stability and reduces unnecessary signaling. Detection probability was analyzed as a function of range, showing stable multi-sensor coverage up to 400 m, while cumulative distribution analysis confirmed end-to-end latency below 50 ms in all test cases. The proposed fusion-aware signaling scheme thus provides a scalable and flexible approach for real-time drone awareness in civil airspace surveillance and smart city surveillance applications
REFERENCES
1. M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Unmanned aerial vehicle with underlay spectrum sharing: A case of drone surveillance,” IEEE Wireless Commun. Lett., vol. 6, no. 4, pp. 518–521, 2017.
2. S. Hayat, E. Yanmaz, and R. Muzaffar, “Survey on unmanned aerial vehicle networks for civil applications,” Ad Hoc Networks, vol. 68, pp. 1–21, 2018.
3. X. Liu and Y. Chen, “Low-latency communications in 6G UAV networks,” IEEE Internet Things J., vol. 8, no. 7, pp. 5371–5384, 2021.
4. M. Erdelj and E. Natalizio, “UAV-assisted disaster management: Applications and open issues,” Proc. IEEE, vol. 105, no. 10, pp. 1872–1897, 2017.
5. A. Fotouhi, M. Ding, and M. Hassan, “Dynamic base station repositioning to improve coverage in UAV networks,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 563–578, 2020.
6. B. Galkin, J. Kibilda, and L. A. DaSilva, “UAVs as mobile infrastructure: Addressing battery lifetime,” IEEE Commun. Mag., vol. 57, no. 6, pp. 132–137, 2019.
7. M. Z. Chowdhury, M. Shahjalal, and Y. M. Jang, “5G wireless communication for smart cities,” ICT Express, vol. 5, no. 2, pp. 77–82, 2019.
8. J. Guo, F. R. Yu, H. Zhang, X. Li, and V. C. M. Leung, “Enabling massive IoT with UAV-based relay systems: Opportunities and challenges,” IEEE Netw., vol. 32, no. 5, pp. 144–151, 2018.
9. T. H. Luan, L. Gao, Z. Li, and D. Zhao, “UAV networks for public safety: Design, challenges, and future directions,” IEEE Access, vol. 7, pp. 42742–42754, 2019.
10. M. Al-Qudaimi, S. M. A. Shah, and H. Al-Raweshidy, “Cognitive UAV communication for low-latency drone operations,” IEEE Access, vol. 9, pp. 118340–118351, 2021.
11. L. Zhang, X. Lin, and Y. Wu, “Delay optimization in multi-hop UAV networks with adaptive scheduling,” IEEE Trans. Veh. Technol., vol. 70, no. 4, pp. 3725–3737, 2021.
12. T. T. Nguyen, P. L. Nguyen, and H. H. Nguyen, “Cooperative detection and data fusion for UAV-assisted threat awareness,” IEEE Sensors J., vol. 22, no. 2, pp. 1178–1188, 2022.
13. C. Chen, Q. Wu, and Z. Zhang, “Fog-assisted UAV monitoring system for intelligent cities,” IEEE Internet Things J., vol. 8, no. 10, pp. 8374–8385, 2021.
14. S. Li and J. Zhou, “Federated fusion learning for UAV-based surveillance,” IEEE Trans. Aerosp. Electron. Syst., vol. 58, no. 2, pp. 1450–1463, 2022.
15. F. Jameel, M. A. Javed, and H. Tabassum, “Hybrid terrestrial–aerial sensing for situational awareness,” IEEE Commun. Lett., vol. 25, no. 9, pp. 2934–2938, 2021
Published
2025-10-31
How to Cite
A. Cengiz, “Low-Latency Data Fusion and Signaling Framework for Civil Drone Monitoring Over Heterogeneous Wireless Networks,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–9, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Neural Network–Based Framework for Automatic Colorization of Grayscale Historical Photographs
Omer Saad Abdulqader Abdulwahab
College of Engineering, University of Information Technology and Communication, Baghdad, Iraq
Muneer Sameer Gheni Mansoor
College of Engineering, University of Information Technology and Communication, Baghdad, Iraq
Hasanien K. Kuba
Biomedical Informatics College, University of Information Technology and Communication, Baghdad, Iraq
Keywords: Image colorization; Neural networks; MATLAB; Grayscale restoration; Deep learning; Computer vision; Historical images.
ABSTRACT:
Colorization of black-and-white images is a difficult and important problem in computer vision, with considerable applications in the restoration and preservation of historical images. This paper describes a neural network–based approach for the automatic colorization of grayscale historical images carried out purely in MATLAB. A feedforward neural network was trained with paired grayscale and colour image datasets in order to learn the mapping from luminance to chrominance components. To save computational cost, the network was trained and tested on low-resolution, downscaled images. As seen from experimental results, the proposed model can generate approximate yet visually acceptable colour reconstructions, clearly identifying key areas such as the sky, vegetation, and human skin. Although the colorized output is not fully photorealistic, the approach validates MATLAB as a powerful and accessible platform for computer vision research and prototyping, particularly in environments where Python toolsets or GPU acceleration are not feasible. This study provides an experimental and educational basis for follow-on research with potential extensions via the addition of convolutional neural networks (CNNs) and larger, more diverse datasets
REFERENCES
1. J. Wang, M. Wan, Y. Xu, X. Kong, G. Gu, and Q. Chen, “Underwater image restoration via constrained color compensation and background light color space-based haze-line model,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
2. J. Wang, M. Wan, Y. Xu, X. Kong, G. Gu, and Q. Chen, “Underwater image restoration via constrained color compensation and background light color space-based haze-line model,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
3. H. S. A. Awan and M. T. Mahmood, “Underwater image restoration through color correction and UW-Net,” Electronics, vol. 13, p. 199, 2024.
4. Q.-K. Ding and H.-E. Liang, “Digital restoration and reconstruction of heritage clothing: a review,” Heritage Science, vol. 12, p. 225, 2024.
5. A. Salmona, L. Bouza, and J. Delon, “Deoldify: A review and implementation of an automatic colorization method,” Image Processing On Line, vol. 12, pp. 347-368, 2022.
6. A. Levin, D. Lischinski, and Y. Weiss, “Colorization using optimization,” in ACM SIGGRAPH 2004 Papers, ed, 2004, pp. 689-694.
7. R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision, 2016, pp. 649-666.
8. S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification,” ACM Transactions on Graphics (ToG), vol. 35, pp. 1-11, 2016.
9. R. Zhang, J.-Y. Zhu, P. Isola, X. Geng, A. S. Lin, T. Yu, et al., “Real-time user-guided image colorization with learned deep priors,” arXiv preprint arXiv:1705.02999, 2017.
10. Y. Cao, Z. Zhou, W. Zhang, and Y. Yu, “Unsupervised diverse colorization via generative adversarial networks,” in Joint European conference on machine learning and knowledge discovery in databases, 2017, pp. 151-166.
11. V. Konovalov and V. Myasnikov, “Study of Colorization and Super-Resolution Efficiency in Image Restoration,” in 2024 X International Conference on Information Technology and Nanotechnology (ITNT), 2024, pp. 1-12>
Published
2025-10-31
How to Cite
O. S. A. Abdulwahab, M. S. G. Mansoor, and H. K. Kuba, “A neural network–based framework for automatic colorization of grayscale historical photographs,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–9, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
Bio-Hybrid Plant-Inspired Soft Robot with Self-Healing and Growth-Like Motion for Environmental Adaptation
Riyadh Jasim Mohammad
Department of Computer Engineering, S.T.C., Islamic Azad University, Tehran, Iran
Keywords : Biomimetics; Soft Robotics; Self-Healing Materials; Hydrogel Actuators; Plant
Tropism; Adaptive Control
ABSTRACT:
In this paper, a new plant-inspired bio-hybrid soft robot with the capability of
emulating natural growth and self-healing behaviors for sustainable and adaptive motion is
proposed. For this, inspiration is taken from plant tropisms such as phototropism and
hydrotropism, enabling the robot to grow or bend autonomously toward light and humidity
stimuli. In the proposed robot, hydrogel-based actuators are integrated with biopolymer sensors
that detect deformation and initiate localized self-healing by way of moisture-induced polymer
crosslinking. A biophysical mathematical model describing the swelling ratio, elongation, and
healing dynamics is developed with the aim of predicting motion and recovery performance.
An enhancement of the control layer is performed by a reinforcement learning approach, where
actuation sequences are optimized to obtain a desired orientation with a minimum amount of
energy. Simulation results obtained in MATLAB show that with the proposed design, a
directional bending angle of 48° toward the light source can be achieved in 20 seconds, while
92% restoration of its mechanical strength can be achieved in 10 minutes after damage. This
work shows that robotic systems may be made sustainable and self-healing using biologically
inspired soft materials along with adaptive learning, thereby finding their application in
environmental monitoring, autonomous exploration, and precision agriculture. This work lays
the foundation for eco-intelligent robotics, in which artificial systems mimic the resilience and
flexibility of natural objects
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1. S. Kim, C. Laschi, and B. Trimmer, “Soft robotics: A bioinspired evolution in
robotics,” Trends in Biotechnology, vol. 31, no. 5, pp. 287–294, 2013.
2. R. F. Shepherd et al., “Multigait soft robot,” Proc. Natl. Acad. Sci. USA, vol. 108,
no. 51, pp. 20400–20403, 2011.
3. D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” Nature,
vol. 521, pp. 467–475, 2015.
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6, pp. 143–153, 2018.
5. O. Moulia and A. Fournier, “The power and control of plant movements,” Nature,
vol. 451, pp. 80–82, 2008.
6. T. Sadeghi et al., “Bioinspired growth-driven soft robotics,” Science Robotics, vol.
5, no. 46, eaaz3910, 2020.
7. R. A. Goriely, The Mathematics of Growth and Morphogenesis. Springer, 2017.
8. Y. Zhao et al., “Light-driven hydrogel actuators with high flexibility and rapid
response,” Adv. Funct. Mater., vol. 28, no. 45, 2018.
9. L. Wu et al., “Self-healing hydrogels for soft robotics,” ACS Appl. Mater.
Interfaces, vol. 13, no. 18, pp. 21392–21406, 2021.
10. L. Hines et al., “Soft actuators for small-scale robotics,” Adv. Mater., vol. 29, no.
13, 2017.
11. F. Iida and B. Trimmer, “Emergent compliance in soft robotic systems,” Soft
Robotics, vol. 5, no. 1, pp. 1–9, 2018.
12. J. Li et al., “pH-responsive hydrogel actuators for biomimetic motion,” ACS Appl.
Mater. Interfaces, vol. 11, pp. 41542–41552, 2019.
13. D.-G. Kim et al., “Dynamic covalent networks for thermally self-healing soft
actuators,” Smart Mater. Struct., vol. 31, no. 7, 075004, 2022.
14. A. Miriyev et al., “Self-healing soft robotic actuators with sensory feedback,” Soft
Robotics Letters, vol. 2, no. 1, pp. 44–53, 2023.
15. P. J. Flory and J. Rehner, “Statistical mechanics of cross-linked polymer
networks,” J. Chem. Phys., vol. 11, no. 11, pp. 512–520, 1943.
16. H. Yuk et al., “Hydrogel robots powered by osmotic pressure,” Nature Commun.,
vol. 11, 2020.
17. X. Wang et al., “Stimuli-responsive hydrogels for soft actuation and sensing,”
Adv. Mater., vol. 34, 2022.
18. S. K. Patel et al., “Self-healing biopolymer composites for soft robotics,”
Polymers, vol. 15, no. 3, 2023.
19. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. MIT
Press, 2018.
20. C. Finn et al., “Model-agnostic meta-learning for fast adaptation of deep
networks,” in Proc. ICML, 2017.
21. M. Y. Liu et al., “Energy-efficient control in soft actuators using reinforcement
learning,” Soft Robotics, vol. 10, no. 2, 2023.
22. L. M. Zhu et al., “Dynamic response of hydrogel actuators under dual stimuli,”
Soft Robotics, vol. 9, no. 1, pp. 56–68, 2022.
23. S. H. Park et al., “Moisture-assisted self-healing of polymer hydrogels for
autonomous robots,” Biomimetics, vol. 8, no. 3, 2023.
24. A. D. Marchese et al., “Autonomous soft robotic fish capable of escape maneuvers
using fluidic elastomer actuators,” Soft Robotics, vol. 1, no. 1, pp. 75–87, 2014.
25. R. Pfeifer and J. Bongard, How the Body Shapes the Way We Think: A New View
of Intelligence. MIT Press, 2006
Published
2025-10-31
How to Cite
R. J. Mohammad, “Bio-Hybrid Plant-Inspired Soft Robot with Self-Healing and Growth-Like Motion for Environmental Adaptation,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–12, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Review of TCP/IP Applications: From Web Services to the Internet of Things (IoT)
Firas Basim Al Hilali
Naif Arab University for Security Sciences, Riyadh 14812, Saudi Arabia
Keywords : TCP/IP, Web Services, IoT Communication, HTTP, IPv6, Network Security,
REST API, IoT Protocols
ABSTRACT:
This review article discusses the evolution of TCP/IP applications from conventional
web services to modern IoT ecosystems. This study examines how classic protocols like HTTP,
FTP, and SMTP influenced the web for communication and how lightweight protocols like
MQTT, CoAP, and 6LoWPAN emerged to support the restrictions of IoT devices. The two
domains have been compared in terms of communication paradigms, security requirements,
scalability concerns, and service quality. Furthermore, protocol enhancements have been
highlighted in this study, such as the use of IPv6, QUIC, DTLS, and the integration with edge
computing. It points out issues that heterogeneous networks are facing nowadays, such as
latency, energy efficiency, device authentication, and congestion control. Finally, it discusses
future directions for researchers and practitioners on safe TCP/IP extensions, AI-driven
network optimization, and 5G-enabled IoT
REFERENCES
1- T. Murkomen, “Performance, privacy, and security issues of TCP/IP at the application layer: A comprehensive survey,” GSC Advanced Research and Reviews, vol. 18, pp. 234-264, 2024.
2- K. Yasukata, “IIP: an integratable TCP/IP stack,” ACM SIGCOMM Computer Communication Review, vol. 54, pp. 21-28, 2024.
3- S.-A. Drăgușin, N. Bizon, R.-M. Teodorescu, D. Toma, R.-N. Boștinaru, and G. Anghel, “Communication Protocols in Embedded Systems for Automotive Applications: Comparative Analysis and Implementation Through Virtual Instruments,” in 2025 17th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2025, pp. 1-8.
4- H. Yang, H. Liu, X. Yuan, K. Wu, W. Ni, J. A. Zhang, et al., “Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems,” Applied Sciences, vol. 15, p. 6587, 2025.
5- A. Gupta and V. K. Chaurasiya, “Adaptive Low-Latency Split Federated Learning with Dynamic Model Partitioning in Resource-Constrained Healthcare IoT,” IEEE Transactions on Green Communications and Networking, 2025.
6- M. M. Alani, “Tcp/ip model,” in Guide to OSI and TCP/IP models, ed: Springer, 2014, pp. 19-50.
7- R. A. A. P. Soepeno, “Comprehensive Network Analysis Through a Single Main Network Architecture,” 2023.
8- J. Wijenbergh, V. Moonsamy, R. van Rijsdijk-Deij, and D. Kuijsters, “Performance comparison of DNS over HTTPS to Unencrypted DNS,” PhD dissertation, 2019.
9- K. R. Fall and W. R. Stevens, Tcp/ip illustrated vol. 1: Addison-Wesley Professional, 2012.
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Published
2025-10-31
How to Cite
F. B. Al Hilali, “A Review of TCP/IP Applications: From Web Services to the Internet of Things (IoT)” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–15, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
Adaptive-SplitAlign: Representation-Aligned Split Learning for Non-IID and Heterogeneous IoT Devices
Worud Mahdi Saleh
General Directorate of Diyala Education, Ministry of Education, Diyala.
Noor Abdulmuttaleb Jaafar
Diyala University Presidency, Diyala. Iraq
Ibtesam Jomaa Hawi
Diyala University Presidency, Diyala. Iraq
Samar Khalil Ibrahim AbdAli
Diyala University Presidency, Diyala. Iraq
ABSTRACT:
Recent developments of edge intelligence have inspired distributed versions of deep learning that can be used to train neural networks without the exchange of raw data. Nevertheless, the majority of out-there split learning approaches presuppose the use of a homogeneous client architecture and an equally distributed dataset, which cannot be applied to the heterogeneous IoT setting. In this paper, we suggest Adaptive-SplitAlign which is a representation-aligned split learning model suggesting to reduce the issue of feature misalignment when using clients with different computational resources and non-IID data distributions. The approach presents 3 novelties: (i) it makes use of representation alignment modules achieved through contrastive or canonical correlation analysis (CCA) goals, to align intermediate features spaces among clients, (ii) it involves a cross-layer aggregation mechanism that builds upon the traditional FedAvg that averages the parameters across network depths, and (iii) it consists of an adaptive early-exit controller that dynamically determines whether a client exits locally or submits features to their server. ResNet-18 is tested in heterogeneous client settings on the CIFAR-10, CIFAR-100 and Tiny-ImageNet datasets using the framework. As demonstrated in the results of the experiment, the Adaptive-SplitAlign achieves a 79.1% top-1 accuracy, which is higher than the Hetero-SplitEE (74.4) and SplitEE (73.1) accuracy. The cost of communication is also lower in Adaptive-SplitAlign (approximately 30). The findings of the ablation tests demonstrate that the alignment module and adaptive exits are valid in stabilizing training during Non-IID conditions. The outlined system offers a viable and extensible collaborative intelligence solution to resource-diverse IoT systems in terms of efficient deployment of models with improved generalization and minimized latency.
REFERENCES
1. T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A Simple Framework for Contrastive Learning of Visual Representations,” Proc. Int. Conf. Machine Learning (ICML), 2020.
2. Z. Lin, X. Hu, Y. Zhang, Z. Chen, Z. Fang, X. Chen, A. Li, P. Vepakomma, and Y. Gao, “FedSplit: Federated Split Learning for Heterogeneous Clients,” IEEE Trans. Neural Networks and Learning Systems, 2023.
3. E. Samikwa, A. Di Maio, and T. Braun, “ARES: Adaptive Resource-Aware Split Learning for Internet of Things,” Computer Networks, vol. 218, 2022.
4. D. J. Bajpai, V. K. Trivedi, S. L. Yadav, and M. K. Hanawal, “SplitEE: Early Exit in Deep Neural Networks with Split Computing,” Proc. AIMS Conf., 2023.
5. D. J. Bajpai, A. Aiswal, and M. K. Hanawal, “I-SplitEE: Image Classification in Split Computing DNNs with Early Exits,” IEEE Int. Conf. Communications (ICC), 2024.
6. Y. Oda, Y. Ono, H. Nakamura, and H. Takase, “Hetero-SplitEE: Split Learning with Early Exits for Heterogeneous IoT Devices,” IEEE MCSoC, 2025.
7. S. Zhang, G. Cheng, Z. Li, and W. Wu, “SplitLLM: Hierarchical Split Learning for Large Language Models over Wireless Networks,” IEEE Globecom Workshops, 2024.
8. Z. Zeng, Y. Hong, H. Dai, H. Zhuang, and C. Chen, “ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Model Inference,” Proc. AAAI Conf. Artificial Intelligence, 2024.
9. J. Ma, X. Lyu, J. Jiang, Q. Cui, H. Yao, and X. Tao, “SplitFrozen: Split Learning with Device-Side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices,” arXiv preprint, 2025.
10. Y. Sun, X. Li, and H. Wang, “Split Federated Learning over Heterogeneous Edge Devices,” IEEE Trans. Mobile Computing, 2024.
11. E. Dritsas and M. Trigka, “Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications,” J. Sens. Actuator Netw., vol. 14, no. 1, 2025.
12. B. Radovič, M. Canini, S. Horváth, V. Pejović, and P. Vepakomma, “Towards a Unified Framework for Split Learning,” Proc. 5th Workshop on Machine Learning and Systems (EuroMLSys ’25), Rotterdam, 2025.
13. Z. Hu, T. Zhou, B. Wu, and Y. Wang, “A Review and Experimental Evaluation on Split Learning,” Future Internet, vol. 17, no. 2, 2025.
14. A. T. Zahir-Ismail et al., “Analyzing the Vulnerabilities in Split Federated Learning,” Sci. Rep., 2025.
15. P. Joshi, C. Thapa, M. Hasanuzzaman, T. Scully, and H. Afli, “Federated Split Learning with Only Positive Labels for Resource-Constrained IoT Environment,” arXiv preprint, Jul. 2023.
Published
2026-2-1
How to Cite
W. M. Saleh, N. A. Jaafar, I. J. Hawi and S. K. I. AbdAli, “Adaptive-SplitAlign: Representation-Aligned Split Learning for Non-IID and Heterogeneous IoT Devices,” Journal of Modern Engineering and Technology, vol. 1, no. 2, pp. 1–9, 2026.
ISSUE
Vol: 1, No 2, 2026
Section
Articles
Adaptive-SplitAlign: Representation-Aligned Split Learning for Non-IID and Heterogeneous IoT Devices
Worud Mahdi Saleh
General Directorate of Diyala Education, Ministry of Education, Diyala.
Noor Abdulmuttaleb Jaafar
Diyala University Presidency, Diyala. Iraq
Ibtesam Jomaa Hawi
Diyala University Presidency, Diyala. Iraq
Samar Khalil Ibrahim AbdAli
Diyala University Presidency, Diyala. Iraq
ABSTRACT:
Recent developments of edge intelligence have inspired distributed versions of deep learning that can be used to train neural networks without the exchange of raw data. Nevertheless, the majority of out-there split learning approaches presuppose the use of a homogeneous client architecture and an equally distributed dataset, which cannot be applied to the heterogeneous IoT setting. In this paper, we suggest Adaptive-SplitAlign which is a representation-aligned split learning model suggesting to reduce the issue of feature misalignment when using clients with different computational resources and non-IID data distributions. The approach presents 3 novelties: (i) it makes use of representation alignment modules achieved through contrastive or canonical correlation analysis (CCA) goals, to align intermediate features spaces among clients, (ii) it involves a cross-layer aggregation mechanism that builds upon the traditional FedAvg that averages the parameters across network depths, and (iii) it consists of an adaptive early-exit controller that dynamically determines whether a client exits locally or submits features to their server. ResNet-18 is tested in heterogeneous client settings on the CIFAR-10, CIFAR-100 and Tiny-ImageNet datasets using the framework. As demonstrated in the results of the experiment, the Adaptive-SplitAlign achieves a 79.1% top-1 accuracy, which is higher than the Hetero-SplitEE (74.4) and SplitEE (73.1) accuracy. The cost of communication is also lower in Adaptive-SplitAlign (approximately 30). The findings of the ablation tests demonstrate that the alignment module and adaptive exits are valid in stabilizing training during Non-IID conditions. The outlined system offers a viable and extensible collaborative intelligence solution to resource-diverse IoT systems in terms of efficient deployment of models with improved generalization and minimized latency.
REFERENCES
1. T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A Simple Framework for Contrastive Learning of Visual Representations,” Proc. Int. Conf. Machine Learning (ICML), 2020.
2. Z. Lin, X. Hu, Y. Zhang, Z. Chen, Z. Fang, X. Chen, A. Li, P. Vepakomma, and Y. Gao, “FedSplit: Federated Split Learning for Heterogeneous Clients,” IEEE Trans. Neural Networks and Learning Systems, 2023.
3. E. Samikwa, A. Di Maio, and T. Braun, “ARES: Adaptive Resource-Aware Split Learning for Internet of Things,” Computer Networks, vol. 218, 2022.
4. D. J. Bajpai, V. K. Trivedi, S. L. Yadav, and M. K. Hanawal, “SplitEE: Early Exit in Deep Neural Networks with Split Computing,” Proc. AIMS Conf., 2023.
5. D. J. Bajpai, A. Aiswal, and M. K. Hanawal, “I-SplitEE: Image Classification in Split Computing DNNs with Early Exits,” IEEE Int. Conf. Communications (ICC), 2024.
6. Y. Oda, Y. Ono, H. Nakamura, and H. Takase, “Hetero-SplitEE: Split Learning with Early Exits for Heterogeneous IoT Devices,” IEEE MCSoC, 2025.
7. S. Zhang, G. Cheng, Z. Li, and W. Wu, “SplitLLM: Hierarchical Split Learning for Large Language Models over Wireless Networks,” IEEE Globecom Workshops, 2024.
8. Z. Zeng, Y. Hong, H. Dai, H. Zhuang, and C. Chen, “ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Model Inference,” Proc. AAAI Conf. Artificial Intelligence, 2024.
9. J. Ma, X. Lyu, J. Jiang, Q. Cui, H. Yao, and X. Tao, “SplitFrozen: Split Learning with Device-Side Model Frozen for Fine-Tuning LLM on Heterogeneous Resource-Constrained Devices,” arXiv preprint, 2025.
10. Y. Sun, X. Li, and H. Wang, “Split Federated Learning over Heterogeneous Edge Devices,” IEEE Trans. Mobile Computing, 2024.
11. E. Dritsas and M. Trigka, “Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications,” J. Sens. Actuator Netw., vol. 14, no. 1, 2025.
12. B. Radovič, M. Canini, S. Horváth, V. Pejović, and P. Vepakomma, “Towards a Unified Framework for Split Learning,” Proc. 5th Workshop on Machine Learning and Systems (EuroMLSys ’25), Rotterdam, 2025.
13. Z. Hu, T. Zhou, B. Wu, and Y. Wang, “A Review and Experimental Evaluation on Split Learning,” Future Internet, vol. 17, no. 2, 2025.
14. A. T. Zahir-Ismail et al., “Analyzing the Vulnerabilities in Split Federated Learning,” Sci. Rep., 2025.
15. P. Joshi, C. Thapa, M. Hasanuzzaman, T. Scully, and H. Afli, “Federated Split Learning with Only Positive Labels for Resource-Constrained IoT Environment,” arXiv preprint, Jul. 2023.
Published
2026-2-1
How to Cite
W. M. Saleh, N. A. Jaafar, I. J. Hawi and S. K. I. AbdAli, “Adaptive-SplitAlign: Representation-Aligned Split Learning for Non-IID and Heterogeneous IoT Devices,” Journal of Modern Engineering and Technology, vol. 1, no. 2, pp. 1–9, 2026.
ISSUE
Vol: 1, No 2, 2026
Section
Articles
Low-Latency Data Fusion and Signaling Framework for Civil Drone Monitoring Over Heterogeneous Wireless Networks
Cengiz Ayten
Postgraduate Information and Computer engineering Institute, Karabük University, Turkey
Keywords: Drone monitoring; Heterogeneous wireless networks; Data fusion; Alert latency; Low-latency communication; multi-sensor system ; Civil airspace surveillance; Real-time signaling; Cooperative detection; Python simulation
ABSTRACT:
This paper presents a low-latency data fusion and signaling model for cooperative civilian drone surveillance in heterogeneous wireless networks. To achieve reaction delay vs. detection confidence tradeoff, the system hybridizes multi-node sensing, local aggregation, and adaptive alert transmission with a tunable data fusion threshold K. A Python-based simulation platform was developed to evaluate the system behaviour under different detection ranges, fusion thresholds, and network conditions. The results show that increasing the fusion threshold K slightly increases the notification delay but significantly improves the notification stability and reduces unnecessary signaling. Detection probability was analyzed as a function of range, showing stable multi-sensor coverage up to 400 m, while cumulative distribution analysis confirmed end-to-end latency below 50 ms in all test cases. The proposed fusion-aware signaling scheme thus provides a scalable and flexible approach for real-time drone awareness in civil airspace surveillance and smart city surveillance applications
REFERENCES
1. M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Unmanned aerial vehicle with underlay spectrum sharing: A case of drone surveillance,” IEEE Wireless Commun. Lett., vol. 6, no. 4, pp. 518–521, 2017.
2. S. Hayat, E. Yanmaz, and R. Muzaffar, “Survey on unmanned aerial vehicle networks for civil applications,” Ad Hoc Networks, vol. 68, pp. 1–21, 2018.
3. X. Liu and Y. Chen, “Low-latency communications in 6G UAV networks,” IEEE Internet Things J., vol. 8, no. 7, pp. 5371–5384, 2021.
4. M. Erdelj and E. Natalizio, “UAV-assisted disaster management: Applications and open issues,” Proc. IEEE, vol. 105, no. 10, pp. 1872–1897, 2017.
5. A. Fotouhi, M. Ding, and M. Hassan, “Dynamic base station repositioning to improve coverage in UAV networks,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 563–578, 2020.
6. B. Galkin, J. Kibilda, and L. A. DaSilva, “UAVs as mobile infrastructure: Addressing battery lifetime,” IEEE Commun. Mag., vol. 57, no. 6, pp. 132–137, 2019.
7. M. Z. Chowdhury, M. Shahjalal, and Y. M. Jang, “5G wireless communication for smart cities,” ICT Express, vol. 5, no. 2, pp. 77–82, 2019.
8. J. Guo, F. R. Yu, H. Zhang, X. Li, and V. C. M. Leung, “Enabling massive IoT with UAV-based relay systems: Opportunities and challenges,” IEEE Netw., vol. 32, no. 5, pp. 144–151, 2018.
9. T. H. Luan, L. Gao, Z. Li, and D. Zhao, “UAV networks for public safety: Design, challenges, and future directions,” IEEE Access, vol. 7, pp. 42742–42754, 2019.
10. M. Al-Qudaimi, S. M. A. Shah, and H. Al-Raweshidy, “Cognitive UAV communication for low-latency drone operations,” IEEE Access, vol. 9, pp. 118340–118351, 2021.
11. L. Zhang, X. Lin, and Y. Wu, “Delay optimization in multi-hop UAV networks with adaptive scheduling,” IEEE Trans. Veh. Technol., vol. 70, no. 4, pp. 3725–3737, 2021.
12. T. T. Nguyen, P. L. Nguyen, and H. H. Nguyen, “Cooperative detection and data fusion for UAV-assisted threat awareness,” IEEE Sensors J., vol. 22, no. 2, pp. 1178–1188, 2022.
13. C. Chen, Q. Wu, and Z. Zhang, “Fog-assisted UAV monitoring system for intelligent cities,” IEEE Internet Things J., vol. 8, no. 10, pp. 8374–8385, 2021.
14. S. Li and J. Zhou, “Federated fusion learning for UAV-based surveillance,” IEEE Trans. Aerosp. Electron. Syst., vol. 58, no. 2, pp. 1450–1463, 2022.
15. F. Jameel, M. A. Javed, and H. Tabassum, “Hybrid terrestrial–aerial sensing for situational awareness,” IEEE Commun. Lett., vol. 25, no. 9, pp. 2934–2938, 2021
Published
2025-10-31
How to Cite
A. Cengiz, “Low-Latency Data Fusion and Signaling Framework for Civil Drone Monitoring Over Heterogeneous Wireless Networks,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–9, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Neural Network–Based Framework for Automatic Colorization of Grayscale Historical Photographs
Omer Saad Abdulqader Abdulwahab
College of Engineering, University of Information Technology and Communication, Baghdad, Iraq
Muneer Sameer Gheni Mansoor
College of Engineering, University of Information Technology and Communication, Baghdad, Iraq
Hasanien K. Kuba
Biomedical Informatics College, University of Information Technology and Communication, Baghdad, Iraq
Keywords: Image colorization; Neural networks; MATLAB; Grayscale restoration; Deep learning; Computer vision; Historical images.
ABSTRACT:
Colorization of black-and-white images is a difficult and important problem in computer vision, with considerable applications in the restoration and preservation of historical images. This paper describes a neural network–based approach for the automatic colorization of grayscale historical images carried out purely in MATLAB. A feedforward neural network was trained with paired grayscale and colour image datasets in order to learn the mapping from luminance to chrominance components. To save computational cost, the network was trained and tested on low-resolution, downscaled images. As seen from experimental results, the proposed model can generate approximate yet visually acceptable colour reconstructions, clearly identifying key areas such as the sky, vegetation, and human skin. Although the colorized output is not fully photorealistic, the approach validates MATLAB as a powerful and accessible platform for computer vision research and prototyping, particularly in environments where Python toolsets or GPU acceleration are not feasible. This study provides an experimental and educational basis for follow-on research with potential extensions via the addition of convolutional neural networks (CNNs) and larger, more diverse datasets
REFERENCES
1. J. Wang, M. Wan, Y. Xu, X. Kong, G. Gu, and Q. Chen, “Underwater image restoration via constrained color compensation and background light color space-based haze-line model,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
2. J. Wang, M. Wan, Y. Xu, X. Kong, G. Gu, and Q. Chen, “Underwater image restoration via constrained color compensation and background light color space-based haze-line model,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
3. H. S. A. Awan and M. T. Mahmood, “Underwater image restoration through color correction and UW-Net,” Electronics, vol. 13, p. 199, 2024.
4. Q.-K. Ding and H.-E. Liang, “Digital restoration and reconstruction of heritage clothing: a review,” Heritage Science, vol. 12, p. 225, 2024.
5. A. Salmona, L. Bouza, and J. Delon, “Deoldify: A review and implementation of an automatic colorization method,” Image Processing On Line, vol. 12, pp. 347-368, 2022.
6. A. Levin, D. Lischinski, and Y. Weiss, “Colorization using optimization,” in ACM SIGGRAPH 2004 Papers, ed, 2004, pp. 689-694.
7. R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision, 2016, pp. 649-666.
8. S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification,” ACM Transactions on Graphics (ToG), vol. 35, pp. 1-11, 2016.
9. R. Zhang, J.-Y. Zhu, P. Isola, X. Geng, A. S. Lin, T. Yu, et al., “Real-time user-guided image colorization with learned deep priors,” arXiv preprint arXiv:1705.02999, 2017.
10. Y. Cao, Z. Zhou, W. Zhang, and Y. Yu, “Unsupervised diverse colorization via generative adversarial networks,” in Joint European conference on machine learning and knowledge discovery in databases, 2017, pp. 151-166.
11. V. Konovalov and V. Myasnikov, “Study of Colorization and Super-Resolution Efficiency in Image Restoration,” in 2024 X International Conference on Information Technology and Nanotechnology (ITNT), 2024, pp. 1-12>
Published
2025-10-31
How to Cite
O. S. A. Abdulwahab, M. S. G. Mansoor, and H. K. Kuba, “A neural network–based framework for automatic colorization of grayscale historical photographs,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–9, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
Bio-Hybrid Plant-Inspired Soft Robot with Self-Healing and Growth-Like Motion for Environmental Adaptation
Riyadh Jasim Mohammad
Department of Computer Engineering, S.T.C., Islamic Azad University, Tehran, Iran
Keywords : Biomimetics; Soft Robotics; Self-Healing Materials; Hydrogel Actuators; Plant
Tropism; Adaptive Control
ABSTRACT:
In this paper, a new plant-inspired bio-hybrid soft robot with the capability of
emulating natural growth and self-healing behaviors for sustainable and adaptive motion is
proposed. For this, inspiration is taken from plant tropisms such as phototropism and
hydrotropism, enabling the robot to grow or bend autonomously toward light and humidity
stimuli. In the proposed robot, hydrogel-based actuators are integrated with biopolymer sensors
that detect deformation and initiate localized self-healing by way of moisture-induced polymer
crosslinking. A biophysical mathematical model describing the swelling ratio, elongation, and
healing dynamics is developed with the aim of predicting motion and recovery performance.
An enhancement of the control layer is performed by a reinforcement learning approach, where
actuation sequences are optimized to obtain a desired orientation with a minimum amount of
energy. Simulation results obtained in MATLAB show that with the proposed design, a
directional bending angle of 48° toward the light source can be achieved in 20 seconds, while
92% restoration of its mechanical strength can be achieved in 10 minutes after damage. This
work shows that robotic systems may be made sustainable and self-healing using biologically
inspired soft materials along with adaptive learning, thereby finding their application in
environmental monitoring, autonomous exploration, and precision agriculture. This work lays
the foundation for eco-intelligent robotics, in which artificial systems mimic the resilience and
flexibility of natural objects
REFERENCES
1. S. Kim, C. Laschi, and B. Trimmer, “Soft robotics: A bioinspired evolution in
robotics,” Trends in Biotechnology, vol. 31, no. 5, pp. 287–294, 2013.
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6, pp. 143–153, 2018.
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5, no. 46, eaaz3910, 2020.
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8. Y. Zhao et al., “Light-driven hydrogel actuators with high flexibility and rapid
response,” Adv. Funct. Mater., vol. 28, no. 45, 2018.
9. L. Wu et al., “Self-healing hydrogels for soft robotics,” ACS Appl. Mater.
Interfaces, vol. 13, no. 18, pp. 21392–21406, 2021.
10. L. Hines et al., “Soft actuators for small-scale robotics,” Adv. Mater., vol. 29, no.
13, 2017.
11. F. Iida and B. Trimmer, “Emergent compliance in soft robotic systems,” Soft
Robotics, vol. 5, no. 1, pp. 1–9, 2018.
12. J. Li et al., “pH-responsive hydrogel actuators for biomimetic motion,” ACS Appl.
Mater. Interfaces, vol. 11, pp. 41542–41552, 2019.
13. D.-G. Kim et al., “Dynamic covalent networks for thermally self-healing soft
actuators,” Smart Mater. Struct., vol. 31, no. 7, 075004, 2022.
14. A. Miriyev et al., “Self-healing soft robotic actuators with sensory feedback,” Soft
Robotics Letters, vol. 2, no. 1, pp. 44–53, 2023.
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networks,” J. Chem. Phys., vol. 11, no. 11, pp. 512–520, 1943.
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vol. 11, 2020.
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Adv. Mater., vol. 34, 2022.
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Polymers, vol. 15, no. 3, 2023.
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Published
2025-10-31
How to Cite
R. J. Mohammad, “Bio-Hybrid Plant-Inspired Soft Robot with Self-Healing and Growth-Like Motion for Environmental Adaptation,” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–12, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Review of TCP/IP Applications: From Web Services to the Internet of Things (IoT)
Firas Basim Al Hilali
Naif Arab University for Security Sciences, Riyadh 14812, Saudi Arabia
Keywords : TCP/IP, Web Services, IoT Communication, HTTP, IPv6, Network Security,
REST API, IoT Protocols
ABSTRACT:
This review article discusses the evolution of TCP/IP applications from conventional
web services to modern IoT ecosystems. This study examines how classic protocols like HTTP,
FTP, and SMTP influenced the web for communication and how lightweight protocols like
MQTT, CoAP, and 6LoWPAN emerged to support the restrictions of IoT devices. The two
domains have been compared in terms of communication paradigms, security requirements,
scalability concerns, and service quality. Furthermore, protocol enhancements have been
highlighted in this study, such as the use of IPv6, QUIC, DTLS, and the integration with edge
computing. It points out issues that heterogeneous networks are facing nowadays, such as
latency, energy efficiency, device authentication, and congestion control. Finally, it discusses
future directions for researchers and practitioners on safe TCP/IP extensions, AI-driven
network optimization, and 5G-enabled IoT
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Published
2025-10-31
How to Cite
F. B. Al Hilali, “A Review of TCP/IP Applications: From Web Services to the Internet of Things (IoT)” Journal of Modern Engineering and Technology, vol. 1, no. 1, pp. 1–15, 2025.
ISSUE
Vol: 1, No 1, 2025
Section
Articles
A Lightweight AI-Driven Intelligent Routing Framework for 6G-Enabled IoT Networks
Nibras Jomaah Mohammed khalaf
Computer Science Department, Al-Imam Al-Adham University College, Baghdad, Iraq
Keywords: Tiny Machine Learning (TinyML); 6G-enabled IoT; Intelligent Routing; Energy-efficient Networks; Lightweight ML; Edge Computing
ABSTRACT:
Internet of Things (IoT) is rapidly growing as the number of connected devices is growing, as well as the need to achieve low latency, high reliability, and high energy consumption communication. As sixth generation (6G) networks are being proposed, the concept of using artificial intelligence within routing protocols has become critical to making IoT systems smarter and self-organising. The present paper suggests a lightweight AI-based routing framework, which uses Tiny Machine Learning (TinyML) models to enable IoT nodes to take local and intelligent routing decisions without imposing computer resource load and raising energy consumption. The proposed framework should achieve energy efficiency, enhance the proportion of packet delivery, and minimize the end-to-end delay, which is done by choosing best routes to be used considering the network parameters like residual energy, signal strength, and degree of congestion. Matlab was used to simulate the framework and compare it to the conventional routing protocols such as AODV, and RPL. The findings indicate that the energy use has greatly improved with a 29 percent reduction in the energy use, a 12 percent increase in the ratio of packet delivery, and a 35 percent decrease in the delay as compared to the traditional techniques. These results emphasize the usefulness of lightweight machine learning methodologies in 6G-enabled IoT networks, and thus the suggested approach is applicable to the application of smart cities, industrial sensor networks, and other massive IoT implementations.
REFERENCES
1- S. Dhanasekar, “A comprehensive review on current issues and advancements of Internet of Things in precision agriculture,” Computer Science Review, vol. 55, p. 100694, 2025.
2- J. Wang, Z. Liu, X. Yang, M. Li, and Z. Lyu, “The Internet of Things under Federated Learning: A Review of the Latest Advances and Applications,” Computers, Materials and Continua, vol. 82, pp. 1-39, 2025.
3- J. M. Kizza, “Internet of things (iot): growth, challenges, and security,” in Guide to computer network security, ed: Springer, 2024, pp. 557-573.
4- M. S. Akbar, Z. Hussain, M. Ikram, Q. Z. Sheng, and S. C. Mukhopadhyay, “On challenges of sixth-generation (6G) wireless networks: A comprehensive survey of requirements, applications, and security issues,” Journal of Network and Computer Applications, vol. 233, p. 104040, 2025.
5- V. Sharma and K. Nayanam, “Sixth Generation (6G) to the Waying Seventh (7G) Wireless Communication Visions and Standards, Challenges, Applications,” Int. J. Adv. Res. Sci. Technol, vol. 13, pp. 1248-1255, 2024.
6- A. Khatoon, W. Wang, M. Wang, L. Li, and A. Ullah, “TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing,” Scientific Reports, vol. 15, p. 20659, 2025.
7- Z. Iqbal, “A TinyML-enabled approach to embed Machine Learning in Avionics Control Systems,” 2024.
8- D. L. Dutta et al., “TinyML meets IoT: A comprehensive survey,” Internet of Things, 2021.
9- S. Heydari et al., “Tiny Machine Learning and On-Device Inference: A Survey,” Sensors, vol. 25, no. 10, 2025.
10- E. A. M. Pereira et al., “An energy-efficient TinyML model for water potability classification on embedded systems,” Science of The Total Environment, 2024.
11- A. Sharma et al., “Optimized TinyML models for IoT anomaly detection,” PLOS ONE, 2025.
12- M. A. Aktas et al., “AI-Enabled Routing in Next-Generation Networks: A Survey,” Alexandria Engineering Journal, 2025.
13- A. Priyadarshi et al., “AI-Based Routing Protocols for Energy-Efficient Wireless Sensor Networks,” Scientific Reports, 2025.
14- S. Das et al., “AI-Optimized Routing Protocol for IoT Networks,” Smart IoT Journal, 2025.
15- A. Zormati et al., “Routing optimization through distributed intelligent softwarization using machine learning,” Neurocomputing, 2025.
16- H. Zhang et al., “Hybrid optimization for efficient IoT traffic management in 6G networks,” Scientific Reports, 2024.
17- J. Huckelberry et al., “TinyML Security: Exploring vulnerabilities in resource-constrained ML systems,” arXiv preprint, 2024.
Published
2026-2-1
How to Cite
N. J. M. khalaf, “A Lightweight AI-Driven Intelligent Routing Framework for 6G-Enabled IoT Networks,” Journal of Modern Engineering and Technology, vol. 1, no. 2, pp. 1–12, 2026.
ISSUE
Vol: 1, No 2, 2026
Section
Articles
A Lightweight AI-Driven Intelligent Routing Framework for 6G-Enabled IoT Networks
Nibras Jomaah Mohammed khalaf
Computer Science Department, Al-Imam Al-Adham University College, Baghdad, Iraq
Keywords: Tiny Machine Learning (TinyML); 6G-enabled IoT; Intelligent Routing; Energy-efficient Networks; Lightweight ML; Edge Computing
ABSTRACT:
Internet of Things (IoT) is rapidly growing as the number of connected devices is growing, as well as the need to achieve low latency, high reliability, and high energy consumption communication. As sixth generation (6G) networks are being proposed, the concept of using artificial intelligence within routing protocols has become critical to making IoT systems smarter and self-organising. The present paper suggests a lightweight AI-based routing framework, which uses Tiny Machine Learning (TinyML) models to enable IoT nodes to take local and intelligent routing decisions without imposing computer resource load and raising energy consumption. The proposed framework should achieve energy efficiency, enhance the proportion of packet delivery, and minimize the end-to-end delay, which is done by choosing best routes to be used considering the network parameters like residual energy, signal strength, and degree of congestion. Matlab was used to simulate the framework and compare it to the conventional routing protocols such as AODV, and RPL. The findings indicate that the energy use has greatly improved with a 29 percent reduction in the energy use, a 12 percent increase in the ratio of packet delivery, and a 35 percent decrease in the delay as compared to the traditional techniques. These results emphasize the usefulness of lightweight machine learning methodologies in 6G-enabled IoT networks, and thus the suggested approach is applicable to the application of smart cities, industrial sensor networks, and other massive IoT implementations.
REFERENCES
1- S. Dhanasekar, “A comprehensive review on current issues and advancements of Internet of Things in precision agriculture,” Computer Science Review, vol. 55, p. 100694, 2025.
2- J. Wang, Z. Liu, X. Yang, M. Li, and Z. Lyu, “The Internet of Things under Federated Learning: A Review of the Latest Advances and Applications,” Computers, Materials and Continua, vol. 82, pp. 1-39, 2025.
3- J. M. Kizza, “Internet of things (iot): growth, challenges, and security,” in Guide to computer network security, ed: Springer, 2024, pp. 557-573.
4- M. S. Akbar, Z. Hussain, M. Ikram, Q. Z. Sheng, and S. C. Mukhopadhyay, “On challenges of sixth-generation (6G) wireless networks: A comprehensive survey of requirements, applications, and security issues,” Journal of Network and Computer Applications, vol. 233, p. 104040, 2025.
5- V. Sharma and K. Nayanam, “Sixth Generation (6G) to the Waying Seventh (7G) Wireless Communication Visions and Standards, Challenges, Applications,” Int. J. Adv. Res. Sci. Technol, vol. 13, pp. 1248-1255, 2024.
6- A. Khatoon, W. Wang, M. Wang, L. Li, and A. Ullah, “TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing,” Scientific Reports, vol. 15, p. 20659, 2025.
7- Z. Iqbal, “A TinyML-enabled approach to embed Machine Learning in Avionics Control Systems,” 2024.
8- D. L. Dutta et al., “TinyML meets IoT: A comprehensive survey,” Internet of Things, 2021.
9- S. Heydari et al., “Tiny Machine Learning and On-Device Inference: A Survey,” Sensors, vol. 25, no. 10, 2025.
10- E. A. M. Pereira et al., “An energy-efficient TinyML model for water potability classification on embedded systems,” Science of The Total Environment, 2024.
11- A. Sharma et al., “Optimized TinyML models for IoT anomaly detection,” PLOS ONE, 2025.
12- M. A. Aktas et al., “AI-Enabled Routing in Next-Generation Networks: A Survey,” Alexandria Engineering Journal, 2025.
13- A. Priyadarshi et al., “AI-Based Routing Protocols for Energy-Efficient Wireless Sensor Networks,” Scientific Reports, 2025.
14- S. Das et al., “AI-Optimized Routing Protocol for IoT Networks,” Smart IoT Journal, 2025.
15- A. Zormati et al., “Routing optimization through distributed intelligent softwarization using machine learning,” Neurocomputing, 2025.
16- H. Zhang et al., “Hybrid optimization for efficient IoT traffic management in 6G networks,” Scientific Reports, 2024.
17- J. Huckelberry et al., “TinyML Security: Exploring vulnerabilities in resource-constrained ML systems,” arXiv preprint, 2024.
Published
2026-2-1
How to Cite
N. J. M. khalaf, “A Lightweight AI-Driven Intelligent Routing Framework for 6G-Enabled IoT Networks,” Journal of Modern Engineering and Technology, vol. 1, no. 2, pp. 1–12, 2026.
ISSUE
Vol: 1, No2, 2026
Section
Articles
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